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Machine Learning
Operators in the Machine Learning category
- 1: Sklearn
- 1.1: Sklearn Training
- 1.1.1: Training: Adaptive Boosting
- 1.1.2: Training: Bagging Training
- 1.1.3: Training: Bernoulli Naive Bayes
- 1.1.4: Training: Complement Naive Bayes
- 1.1.5: Training: Decision Tree
- 1.1.6: Training: Dummy Classifier
- 1.1.7: Training: Extra Tree
- 1.1.8: Training: Extra Trees
- 1.1.9: Training: Gaussian Naive Bayes
- 1.1.10: Training: Gradient Boosting
- 1.1.11: Training: K-nearest Neighbors
- 1.1.12: Training: Linear Perceptron
- 1.1.13: Training: Linear Regression
- 1.1.14: Training: Linear Support Vector Machine
- 1.1.15: Training: Logistic Regression
- 1.1.16: Training: Logistic Regression Cross Validation
- 1.1.17: Training: Multi-layer Perceptron
- 1.1.18: Training: Multinomial Naive Bayes
- 1.1.19: Training: Nearest Centroid
- 1.1.20: Training: Passive Aggressive
- 1.1.21: Training: Probability Calibration
- 1.1.22: Training: Random Forest
- 1.1.23: Training: Ridge Regression
- 1.1.24: Training: Ridge Regression Cross Validation
- 1.1.25: Training: Stochastic Gradient Descent
- 1.1.26: Training: Support Vector Machine
- 1.2: Adaptive Boosting
- 1.3: Bagging
- 1.4: Bernoulli Naive Bayes
- 1.5: Complement Naive Bayes
- 1.6: Decision Tree
- 1.7: Dummy Classifier
- 1.8: Extra Tree
- 1.9: Extra Trees
- 1.10: Gaussian Naive Bayes
- 1.11: Gradient Boosting
- 1.12: K-nearest Neighbors
- 1.13: Linear Perceptron
- 1.14: Linear Regression
- 1.15: Linear Support Vector Machine
- 1.16: Logistic Regression
- 1.17: Logistic Regression Cross Validation
- 1.18: Multi-layer Perceptron
- 1.19: Multinomial Naive Bayes
- 1.20: Nearest Centroid
- 1.21: Passive Aggressive
- 1.22: Probability Calibration
- 1.23: Random Forest
- 1.24: Ridge Regression
- 1.25: Ridge Regression Cross Validation
- 1.26: Sklearn Prediction
- 1.27: Sklearn Testing
- 1.28: Stochastic Gradient Descent
- 1.29: Support Vector Machine
- 2: Advanced Sklearn
- 2.1: KNN Classifier
- 2.2: KNN Regressor
- 2.3: SVM Classifier
- 2.4: SVM Regressor
- 3: Hugging Face
- 3.1: Hugging Face Iris Logistic Regression
- 3.2: Hugging Face Sentiment Analysis
- 3.3: Hugging Face Spam Detection
- 3.4: Hugging Face Text Summarization
- 4: Machine Learning General
1 - Sklearn
Operators in the Sklearn category
Home > Machine Learning > Sklearn
Subcategories
Operators
| Operator | Description |
|---|---|
| Adaptive Boosting | Sklearn Adaptive Boosting Operator |
| Bagging | Sklearn Bagging Operator |
| Bernoulli Naive Bayes | Sklearn Bernoulli Naive Bayes Operator |
| Complement Naive Bayes | Sklearn Complement Naive Bayes Operator |
| Decision Tree | Sklearn Decision Tree Operator |
| Dummy Classifier | Sklearn Dummy Classifier Operator |
| Extra Tree | Sklearn Extra Tree Operator |
| Extra Trees | Sklearn Extra Trees Operator |
| Gaussian Naive Bayes | Sklearn Gaussian Naive Bayes Operator |
| Gradient Boosting | Sklearn Gradient Boosting Operator |
| K-nearest Neighbors | Sklearn K-nearest Neighbors Operator |
| Linear Regression | Sklearn Linear Regression Operator |
| Linear Support Vector Machine | Sklearn Linear Support Vector Machine Operator |
| Logistic Regression | Sklearn Logistic Regression Operator |
| Logistic Regression Cross Validation | Sklearn Logistic Regression Cross Validation Operator |
| Multi-layer Perceptron | Sklearn Multi-layer Perceptron Operator |
| Multinomial Naive Bayes | Sklearn Multinomial Naive Bayes Operator |
| Nearest Centroid | Sklearn Nearest Centroid Operator |
| Passive Aggressive | Sklearn Passive Aggressive Operator |
| Linear Perceptron | Sklearn Linear Perceptron Operator |
| Sklearn Prediction | Sklearn Prediction Operator |
| Probability Calibration | Sklearn Probability Calibration Operator |
| Random Forest | Sklearn Random Forest Operator |
| Ridge Regression | Sklearn Ridge Regression Operator |
| Ridge Regression Cross Validation | Sklearn Ridge Regression Cross Validation Operator |
| Stochastic Gradient Descent | Sklearn Stochastic Gradient Descent Operator |
| Support Vector Machine | Sklearn Support Vector Machine Operator |
| Sklearn Testing | It will generate scorers for Sklearn model |
Total: 28 operators
1.1 - Sklearn Training
Operators in the Sklearn Training category
Home > Sklearn > Sklearn Training
Operators
| Operator | Description |
|---|---|
| Training: Adaptive Boosting | Sklearn Training: Adaptive Boosting Operator |
| Training: Bagging Training | Sklearn Training: Bagging Training Operator |
| Training: Bernoulli Naive Bayes | Sklearn Training: Bernoulli Naive Bayes Operator |
| Training: Complement Naive Bayes | Sklearn Training: Complement Naive Bayes Operator |
| Training: Decision Tree | Sklearn Training: Decision Tree Operator |
| Training: Dummy Classifier | Sklearn Training: Dummy Classifier Operator |
| Training: Extra Tree | Sklearn Training: Extra Tree Operator |
| Training: Extra Trees | Sklearn Training: Extra Trees Operator |
| Training: Gaussian Naive Bayes | Sklearn Training: Gaussian Naive Bayes Operator |
| Training: Gradient Boosting | Sklearn Training: Gradient Boosting Operator |
| Training: K-nearest Neighbors | Sklearn Training: K-nearest Neighbors Operator |
| Training: Linear Regression | Sklearn Training: Linear Regression Operator |
| Training: Linear Support Vector Machine | Sklearn Training: Linear Support Vector Machine Operator |
| Training: Logistic Regression | Sklearn Training: Logistic Regression Operator |
| Training: Logistic Regression Cross Validation | Sklearn Training: Logistic Regression Cross Validation Operator |
| Training: Multi-layer Perceptron | Sklearn Training: Multi-layer Perceptron Operator |
| Training: Multinomial Naive Bayes | Sklearn Training: Multinomial Naive Bayes Operator |
| Training: Nearest Centroid | Sklearn Training: Nearest Centroid Operator |
| Training: Passive Aggressive | Sklearn Training: Passive Aggressive Operator |
| Training: Linear Perceptron | Sklearn Training: Linear Perceptron Operator |
| Training: Probability Calibration | Sklearn Training: Probability Calibration Operator |
| Training: Random Forest | Sklearn Training: Random Forest Operator |
| Training: Ridge Regression | Sklearn Training: Ridge Regression Operator |
| Training: Ridge Regression Cross Validation | Sklearn Training: Ridge Regression Cross Validation Operator |
| Training: Stochastic Gradient Descent | Sklearn Training: Stochastic Gradient Descent Operator |
| Training: Support Vector Machine | Sklearn Training: Support Vector Machine Operator |
Total: 26 operators
1.1.1 - Training: Adaptive Boosting
Sklearn Training: Adaptive Boosting Operator
Home > Machine Learning > Sklearn > Sklearn Training
Input Properties
| Property | Requirement | Type | Default | Description |
|---|---|---|---|---|
| Target Attribute | ✓ | String | - | Attribute in your dataset corresponding to target |
| Count Vectorizer | Boolean | false | Convert a collection of text documents to a matrix of token counts | |
| ↳ Text Attribute | String | - | Attribute in your dataset with text to vectorize | |
| ↳ Tfidf Transformer | Boolean | false | Transform a count matrix to a normalized tf or tf-idf representation |
Output Ports
| Port | Mode |
|---|---|
| 0 | Set Snapshot |
1.1.2 - Training: Bagging Training
Sklearn Training: Bagging Training Operator
Home > Machine Learning > Sklearn > Sklearn Training
Input Properties
| Property | Requirement | Type | Default | Description |
|---|---|---|---|---|
| Target Attribute | ✓ | String | - | Attribute in your dataset corresponding to target |
| Count Vectorizer | Boolean | false | Convert a collection of text documents to a matrix of token counts | |
| ↳ Text Attribute | String | - | Attribute in your dataset with text to vectorize | |
| ↳ Tfidf Transformer | Boolean | false | Transform a count matrix to a normalized tf or tf-idf representation |
Output Ports
| Port | Mode |
|---|---|
| 0 | Set Snapshot |
1.1.3 - Training: Bernoulli Naive Bayes
Sklearn Training: Bernoulli Naive Bayes Operator
Home > Machine Learning > Sklearn > Sklearn Training
Input Properties
| Property | Requirement | Type | Default | Description |
|---|---|---|---|---|
| Target Attribute | ✓ | String | - | Attribute in your dataset corresponding to target |
| Count Vectorizer | Boolean | false | Convert a collection of text documents to a matrix of token counts | |
| ↳ Text Attribute | String | - | Attribute in your dataset with text to vectorize | |
| ↳ Tfidf Transformer | Boolean | false | Transform a count matrix to a normalized tf or tf-idf representation |
Output Ports
| Port | Mode |
|---|---|
| 0 | Set Snapshot |
1.1.4 - Training: Complement Naive Bayes
Sklearn Training: Complement Naive Bayes Operator
Home > Machine Learning > Sklearn > Sklearn Training
Input Properties
| Property | Requirement | Type | Default | Description |
|---|---|---|---|---|
| Target Attribute | ✓ | String | - | Attribute in your dataset corresponding to target |
| Count Vectorizer | Boolean | false | Convert a collection of text documents to a matrix of token counts | |
| ↳ Text Attribute | String | - | Attribute in your dataset with text to vectorize | |
| ↳ Tfidf Transformer | Boolean | false | Transform a count matrix to a normalized tf or tf-idf representation |
Output Ports
| Port | Mode |
|---|---|
| 0 | Set Snapshot |
1.1.5 - Training: Decision Tree
Sklearn Training: Decision Tree Operator
Home > Machine Learning > Sklearn > Sklearn Training
Input Properties
| Property | Requirement | Type | Default | Description |
|---|---|---|---|---|
| Target Attribute | ✓ | String | - | Attribute in your dataset corresponding to target |
| Count Vectorizer | Boolean | false | Convert a collection of text documents to a matrix of token counts | |
| ↳ Text Attribute | String | - | Attribute in your dataset with text to vectorize | |
| ↳ Tfidf Transformer | Boolean | false | Transform a count matrix to a normalized tf or tf-idf representation |
Output Ports
| Port | Mode |
|---|---|
| 0 | Set Snapshot |
1.1.6 - Training: Dummy Classifier
Sklearn Training: Dummy Classifier Operator
Home > Machine Learning > Sklearn > Sklearn Training
Input Properties
| Property | Requirement | Type | Default | Description |
|---|---|---|---|---|
| Target Attribute | ✓ | String | - | Attribute in your dataset corresponding to target |
| Count Vectorizer | Boolean | false | Convert a collection of text documents to a matrix of token counts | |
| ↳ Text Attribute | String | - | Attribute in your dataset with text to vectorize | |
| ↳ Tfidf Transformer | Boolean | false | Transform a count matrix to a normalized tf or tf-idf representation |
Output Ports
| Port | Mode |
|---|---|
| 0 | Set Snapshot |
1.1.7 - Training: Extra Tree
Sklearn Training: Extra Tree Operator
Home > Machine Learning > Sklearn > Sklearn Training
Input Properties
| Property | Requirement | Type | Default | Description |
|---|---|---|---|---|
| Target Attribute | ✓ | String | - | Attribute in your dataset corresponding to target |
| Count Vectorizer | Boolean | false | Convert a collection of text documents to a matrix of token counts | |
| ↳ Text Attribute | String | - | Attribute in your dataset with text to vectorize | |
| ↳ Tfidf Transformer | Boolean | false | Transform a count matrix to a normalized tf or tf-idf representation |
Output Ports
| Port | Mode |
|---|---|
| 0 | Set Snapshot |
1.1.8 - Training: Extra Trees
Sklearn Training: Extra Trees Operator
Home > Machine Learning > Sklearn > Sklearn Training
Input Properties
| Property | Requirement | Type | Default | Description |
|---|---|---|---|---|
| Target Attribute | ✓ | String | - | Attribute in your dataset corresponding to target |
| Count Vectorizer | Boolean | false | Convert a collection of text documents to a matrix of token counts | |
| ↳ Text Attribute | String | - | Attribute in your dataset with text to vectorize | |
| ↳ Tfidf Transformer | Boolean | false | Transform a count matrix to a normalized tf or tf-idf representation |
Output Ports
| Port | Mode |
|---|---|
| 0 | Set Snapshot |
1.1.9 - Training: Gaussian Naive Bayes
Sklearn Training: Gaussian Naive Bayes Operator
Home > Machine Learning > Sklearn > Sklearn Training
Input Properties
| Property | Requirement | Type | Default | Description |
|---|---|---|---|---|
| Target Attribute | ✓ | String | - | Attribute in your dataset corresponding to target |
| Count Vectorizer | Boolean | false | Convert a collection of text documents to a matrix of token counts | |
| ↳ Text Attribute | String | - | Attribute in your dataset with text to vectorize | |
| ↳ Tfidf Transformer | Boolean | false | Transform a count matrix to a normalized tf or tf-idf representation |
Output Ports
| Port | Mode |
|---|---|
| 0 | Set Snapshot |
1.1.10 - Training: Gradient Boosting
Sklearn Training: Gradient Boosting Operator
Home > Machine Learning > Sklearn > Sklearn Training
Input Properties
| Property | Requirement | Type | Default | Description |
|---|---|---|---|---|
| Target Attribute | ✓ | String | - | Attribute in your dataset corresponding to target |
| Count Vectorizer | Boolean | false | Convert a collection of text documents to a matrix of token counts | |
| ↳ Text Attribute | String | - | Attribute in your dataset with text to vectorize | |
| ↳ Tfidf Transformer | Boolean | false | Transform a count matrix to a normalized tf or tf-idf representation |
Output Ports
| Port | Mode |
|---|---|
| 0 | Set Snapshot |
1.1.11 - Training: K-nearest Neighbors
Sklearn Training: K-nearest Neighbors Operator
Home > Machine Learning > Sklearn > Sklearn Training
Input Properties
| Property | Requirement | Type | Default | Description |
|---|---|---|---|---|
| Target Attribute | ✓ | String | - | Attribute in your dataset corresponding to target |
| Count Vectorizer | Boolean | false | Convert a collection of text documents to a matrix of token counts | |
| ↳ Text Attribute | String | - | Attribute in your dataset with text to vectorize | |
| ↳ Tfidf Transformer | Boolean | false | Transform a count matrix to a normalized tf or tf-idf representation |
Output Ports
| Port | Mode |
|---|---|
| 0 | Set Snapshot |
1.1.12 - Training: Linear Perceptron
Sklearn Training: Linear Perceptron Operator
Home > Machine Learning > Sklearn > Sklearn Training
Input Properties
| Property | Requirement | Type | Default | Description |
|---|---|---|---|---|
| Target Attribute | ✓ | String | - | Attribute in your dataset corresponding to target |
| Count Vectorizer | Boolean | false | Convert a collection of text documents to a matrix of token counts | |
| ↳ Text Attribute | String | - | Attribute in your dataset with text to vectorize | |
| ↳ Tfidf Transformer | Boolean | false | Transform a count matrix to a normalized tf or tf-idf representation |
Output Ports
| Port | Mode |
|---|---|
| 0 | Set Snapshot |
1.1.13 - Training: Linear Regression
Sklearn Training: Linear Regression Operator
Home > Machine Learning > Sklearn > Sklearn Training
Input Properties
| Property | Requirement | Type | Default | Description |
|---|---|---|---|---|
| Target Attribute | ✓ | String | - | Attribute in your dataset corresponding to target |
| Count Vectorizer | Boolean | false | Convert a collection of text documents to a matrix of token counts | |
| ↳ Text Attribute | String | - | Attribute in your dataset with text to vectorize | |
| ↳ Tfidf Transformer | Boolean | false | Transform a count matrix to a normalized tf or tf-idf representation |
Output Ports
| Port | Mode |
|---|---|
| 0 | Set Snapshot |
1.1.14 - Training: Linear Support Vector Machine
Sklearn Training: Linear Support Vector Machine Operator
Home > Machine Learning > Sklearn > Sklearn Training
Input Properties
| Property | Requirement | Type | Default | Description |
|---|---|---|---|---|
| Target Attribute | ✓ | String | - | Attribute in your dataset corresponding to target |
| Count Vectorizer | Boolean | false | Convert a collection of text documents to a matrix of token counts | |
| ↳ Text Attribute | String | - | Attribute in your dataset with text to vectorize | |
| ↳ Tfidf Transformer | Boolean | false | Transform a count matrix to a normalized tf or tf-idf representation |
Output Ports
| Port | Mode |
|---|---|
| 0 | Set Snapshot |
1.1.15 - Training: Logistic Regression
Sklearn Training: Logistic Regression Operator
Home > Machine Learning > Sklearn > Sklearn Training
Input Properties
| Property | Requirement | Type | Default | Description |
|---|---|---|---|---|
| Target Attribute | ✓ | String | - | Attribute in your dataset corresponding to target |
| Count Vectorizer | Boolean | false | Convert a collection of text documents to a matrix of token counts | |
| ↳ Text Attribute | String | - | Attribute in your dataset with text to vectorize | |
| ↳ Tfidf Transformer | Boolean | false | Transform a count matrix to a normalized tf or tf-idf representation |
Output Ports
| Port | Mode |
|---|---|
| 0 | Set Snapshot |
1.1.16 - Training: Logistic Regression Cross Validation
Sklearn Training: Logistic Regression Cross Validation Operator
Home > Machine Learning > Sklearn > Sklearn Training
Input Properties
| Property | Requirement | Type | Default | Description |
|---|---|---|---|---|
| Target Attribute | ✓ | String | - | Attribute in your dataset corresponding to target |
| Count Vectorizer | Boolean | false | Convert a collection of text documents to a matrix of token counts | |
| ↳ Text Attribute | String | - | Attribute in your dataset with text to vectorize | |
| ↳ Tfidf Transformer | Boolean | false | Transform a count matrix to a normalized tf or tf-idf representation |
Output Ports
| Port | Mode |
|---|---|
| 0 | Set Snapshot |
1.1.17 - Training: Multi-layer Perceptron
Sklearn Training: Multi-layer Perceptron Operator
Home > Machine Learning > Sklearn > Sklearn Training
Input Properties
| Property | Requirement | Type | Default | Description |
|---|---|---|---|---|
| Target Attribute | ✓ | String | - | Attribute in your dataset corresponding to target |
| Count Vectorizer | Boolean | false | Convert a collection of text documents to a matrix of token counts | |
| ↳ Text Attribute | String | - | Attribute in your dataset with text to vectorize | |
| ↳ Tfidf Transformer | Boolean | false | Transform a count matrix to a normalized tf or tf-idf representation |
Output Ports
| Port | Mode |
|---|---|
| 0 | Set Snapshot |
1.1.18 - Training: Multinomial Naive Bayes
Sklearn Training: Multinomial Naive Bayes Operator
Home > Machine Learning > Sklearn > Sklearn Training
Input Properties
| Property | Requirement | Type | Default | Description |
|---|---|---|---|---|
| Target Attribute | ✓ | String | - | Attribute in your dataset corresponding to target |
| Count Vectorizer | Boolean | false | Convert a collection of text documents to a matrix of token counts | |
| ↳ Text Attribute | String | - | Attribute in your dataset with text to vectorize | |
| ↳ Tfidf Transformer | Boolean | false | Transform a count matrix to a normalized tf or tf-idf representation |
Output Ports
| Port | Mode |
|---|---|
| 0 | Set Snapshot |
1.1.19 - Training: Nearest Centroid
Sklearn Training: Nearest Centroid Operator
Home > Machine Learning > Sklearn > Sklearn Training
Input Properties
| Property | Requirement | Type | Default | Description |
|---|---|---|---|---|
| Target Attribute | ✓ | String | - | Attribute in your dataset corresponding to target |
| Count Vectorizer | Boolean | false | Convert a collection of text documents to a matrix of token counts | |
| ↳ Text Attribute | String | - | Attribute in your dataset with text to vectorize | |
| ↳ Tfidf Transformer | Boolean | false | Transform a count matrix to a normalized tf or tf-idf representation |
Output Ports
| Port | Mode |
|---|---|
| 0 | Set Snapshot |
1.1.20 - Training: Passive Aggressive
Sklearn Training: Passive Aggressive Operator
Home > Machine Learning > Sklearn > Sklearn Training
Input Properties
| Property | Requirement | Type | Default | Description |
|---|---|---|---|---|
| Target Attribute | ✓ | String | - | Attribute in your dataset corresponding to target |
| Count Vectorizer | Boolean | false | Convert a collection of text documents to a matrix of token counts | |
| ↳ Text Attribute | String | - | Attribute in your dataset with text to vectorize | |
| ↳ Tfidf Transformer | Boolean | false | Transform a count matrix to a normalized tf or tf-idf representation |
Output Ports
| Port | Mode |
|---|---|
| 0 | Set Snapshot |
1.1.21 - Training: Probability Calibration
Sklearn Training: Probability Calibration Operator
Home > Machine Learning > Sklearn > Sklearn Training
Input Properties
| Property | Requirement | Type | Default | Description |
|---|---|---|---|---|
| Target Attribute | ✓ | String | - | Attribute in your dataset corresponding to target |
| Count Vectorizer | Boolean | false | Convert a collection of text documents to a matrix of token counts | |
| ↳ Text Attribute | String | - | Attribute in your dataset with text to vectorize | |
| ↳ Tfidf Transformer | Boolean | false | Transform a count matrix to a normalized tf or tf-idf representation |
Output Ports
| Port | Mode |
|---|---|
| 0 | Set Snapshot |
1.1.22 - Training: Random Forest
Sklearn Training: Random Forest Operator
Home > Machine Learning > Sklearn > Sklearn Training
Input Properties
| Property | Requirement | Type | Default | Description |
|---|---|---|---|---|
| Target Attribute | ✓ | String | - | Attribute in your dataset corresponding to target |
| Count Vectorizer | Boolean | false | Convert a collection of text documents to a matrix of token counts | |
| ↳ Text Attribute | String | - | Attribute in your dataset with text to vectorize | |
| ↳ Tfidf Transformer | Boolean | false | Transform a count matrix to a normalized tf or tf-idf representation |
Output Ports
| Port | Mode |
|---|---|
| 0 | Set Snapshot |
1.1.23 - Training: Ridge Regression
Sklearn Training: Ridge Regression Operator
Home > Machine Learning > Sklearn > Sklearn Training
Input Properties
| Property | Requirement | Type | Default | Description |
|---|---|---|---|---|
| Target Attribute | ✓ | String | - | Attribute in your dataset corresponding to target |
| Count Vectorizer | Boolean | false | Convert a collection of text documents to a matrix of token counts | |
| ↳ Text Attribute | String | - | Attribute in your dataset with text to vectorize | |
| ↳ Tfidf Transformer | Boolean | false | Transform a count matrix to a normalized tf or tf-idf representation |
Output Ports
| Port | Mode |
|---|---|
| 0 | Set Snapshot |
1.1.24 - Training: Ridge Regression Cross Validation
Sklearn Training: Ridge Regression Cross Validation Operator
Home > Machine Learning > Sklearn > Sklearn Training
Input Properties
| Property | Requirement | Type | Default | Description |
|---|---|---|---|---|
| Target Attribute | ✓ | String | - | Attribute in your dataset corresponding to target |
| Count Vectorizer | Boolean | false | Convert a collection of text documents to a matrix of token counts | |
| ↳ Text Attribute | String | - | Attribute in your dataset with text to vectorize | |
| ↳ Tfidf Transformer | Boolean | false | Transform a count matrix to a normalized tf or tf-idf representation |
Output Ports
| Port | Mode |
|---|---|
| 0 | Set Snapshot |
1.1.25 - Training: Stochastic Gradient Descent
Sklearn Training: Stochastic Gradient Descent Operator
Home > Machine Learning > Sklearn > Sklearn Training
Input Properties
| Property | Requirement | Type | Default | Description |
|---|---|---|---|---|
| Target Attribute | ✓ | String | - | Attribute in your dataset corresponding to target |
| Count Vectorizer | Boolean | false | Convert a collection of text documents to a matrix of token counts | |
| ↳ Text Attribute | String | - | Attribute in your dataset with text to vectorize | |
| ↳ Tfidf Transformer | Boolean | false | Transform a count matrix to a normalized tf or tf-idf representation |
Output Ports
| Port | Mode |
|---|---|
| 0 | Set Snapshot |
1.1.26 - Training: Support Vector Machine
Sklearn Training: Support Vector Machine Operator
Home > Machine Learning > Sklearn > Sklearn Training
Input Properties
| Property | Requirement | Type | Default | Description |
|---|---|---|---|---|
| Target Attribute | ✓ | String | - | Attribute in your dataset corresponding to target |
| Count Vectorizer | Boolean | false | Convert a collection of text documents to a matrix of token counts | |
| ↳ Text Attribute | String | - | Attribute in your dataset with text to vectorize | |
| ↳ Tfidf Transformer | Boolean | false | Transform a count matrix to a normalized tf or tf-idf representation |
Output Ports
| Port | Mode |
|---|---|
| 0 | Set Snapshot |
1.2 - Adaptive Boosting
Sklearn Adaptive Boosting Operator
Home > Machine Learning > Sklearn
Input Properties
| Property | Requirement | Type | Default | Description |
|---|---|---|---|---|
| Target Attribute | ✓ | String | - | Attribute in your dataset corresponding to target |
| Count Vectorizer | Boolean | false | Convert a collection of text documents to a matrix of token counts | |
| ↳ Text Attribute | String | - | Attribute in your dataset with text to vectorize | |
| ↳ Tfidf Transformer | Boolean | false | Transform a count matrix to a normalized tf or tf-idf representation |
Output Ports
| Port | Mode |
|---|---|
| 0 | Set Snapshot |
1.3 - Bagging
Sklearn Bagging Operator
Home > Machine Learning > Sklearn
Input Properties
| Property | Requirement | Type | Default | Description |
|---|---|---|---|---|
| Target Attribute | ✓ | String | - | Attribute in your dataset corresponding to target |
| Count Vectorizer | Boolean | false | Convert a collection of text documents to a matrix of token counts | |
| ↳ Text Attribute | String | - | Attribute in your dataset with text to vectorize | |
| ↳ Tfidf Transformer | Boolean | false | Transform a count matrix to a normalized tf or tf-idf representation |
Output Ports
| Port | Mode |
|---|---|
| 0 | Set Snapshot |
1.4 - Bernoulli Naive Bayes
Sklearn Bernoulli Naive Bayes Operator
Home > Machine Learning > Sklearn
Input Properties
| Property | Requirement | Type | Default | Description |
|---|---|---|---|---|
| Target Attribute | ✓ | String | - | Attribute in your dataset corresponding to target |
| Count Vectorizer | Boolean | false | Convert a collection of text documents to a matrix of token counts | |
| ↳ Text Attribute | String | - | Attribute in your dataset with text to vectorize | |
| ↳ Tfidf Transformer | Boolean | false | Transform a count matrix to a normalized tf or tf-idf representation |
Output Ports
| Port | Mode |
|---|---|
| 0 | Set Snapshot |
1.5 - Complement Naive Bayes
Sklearn Complement Naive Bayes Operator
Home > Machine Learning > Sklearn
Input Properties
| Property | Requirement | Type | Default | Description |
|---|---|---|---|---|
| Target Attribute | ✓ | String | - | Attribute in your dataset corresponding to target |
| Count Vectorizer | Boolean | false | Convert a collection of text documents to a matrix of token counts | |
| ↳ Text Attribute | String | - | Attribute in your dataset with text to vectorize | |
| ↳ Tfidf Transformer | Boolean | false | Transform a count matrix to a normalized tf or tf-idf representation |
Output Ports
| Port | Mode |
|---|---|
| 0 | Set Snapshot |
1.6 - Decision Tree
Sklearn Decision Tree Operator
Home > Machine Learning > Sklearn
Input Properties
| Property | Requirement | Type | Default | Description |
|---|---|---|---|---|
| Target Attribute | ✓ | String | - | Attribute in your dataset corresponding to target |
| Count Vectorizer | Boolean | false | Convert a collection of text documents to a matrix of token counts | |
| ↳ Text Attribute | String | - | Attribute in your dataset with text to vectorize | |
| ↳ Tfidf Transformer | Boolean | false | Transform a count matrix to a normalized tf or tf-idf representation |
Output Ports
| Port | Mode |
|---|---|
| 0 | Set Snapshot |
1.7 - Dummy Classifier
Sklearn Dummy Classifier Operator
Home > Machine Learning > Sklearn
Input Properties
| Property | Requirement | Type | Default | Description |
|---|---|---|---|---|
| Target Attribute | ✓ | String | - | Attribute in your dataset corresponding to target |
| Count Vectorizer | Boolean | false | Convert a collection of text documents to a matrix of token counts | |
| ↳ Text Attribute | String | - | Attribute in your dataset with text to vectorize | |
| ↳ Tfidf Transformer | Boolean | false | Transform a count matrix to a normalized tf or tf-idf representation |
Output Ports
| Port | Mode |
|---|---|
| 0 | Set Snapshot |
1.8 - Extra Tree
Sklearn Extra Tree Operator
Home > Machine Learning > Sklearn
Input Properties
| Property | Requirement | Type | Default | Description |
|---|---|---|---|---|
| Target Attribute | ✓ | String | - | Attribute in your dataset corresponding to target |
| Count Vectorizer | Boolean | false | Convert a collection of text documents to a matrix of token counts | |
| ↳ Text Attribute | String | - | Attribute in your dataset with text to vectorize | |
| ↳ Tfidf Transformer | Boolean | false | Transform a count matrix to a normalized tf or tf-idf representation |
Output Ports
| Port | Mode |
|---|---|
| 0 | Set Snapshot |
1.9 - Extra Trees
Sklearn Extra Trees Operator
Home > Machine Learning > Sklearn
Input Properties
| Property | Requirement | Type | Default | Description |
|---|---|---|---|---|
| Target Attribute | ✓ | String | - | Attribute in your dataset corresponding to target |
| Count Vectorizer | Boolean | false | Convert a collection of text documents to a matrix of token counts | |
| ↳ Text Attribute | String | - | Attribute in your dataset with text to vectorize | |
| ↳ Tfidf Transformer | Boolean | false | Transform a count matrix to a normalized tf or tf-idf representation |
Output Ports
| Port | Mode |
|---|---|
| 0 | Set Snapshot |
1.10 - Gaussian Naive Bayes
Sklearn Gaussian Naive Bayes Operator
Home > Machine Learning > Sklearn
Input Properties
| Property | Requirement | Type | Default | Description |
|---|---|---|---|---|
| Target Attribute | ✓ | String | - | Attribute in your dataset corresponding to target |
| Count Vectorizer | Boolean | false | Convert a collection of text documents to a matrix of token counts | |
| ↳ Text Attribute | String | - | Attribute in your dataset with text to vectorize | |
| ↳ Tfidf Transformer | Boolean | false | Transform a count matrix to a normalized tf or tf-idf representation |
Output Ports
| Port | Mode |
|---|---|
| 0 | Set Snapshot |
1.11 - Gradient Boosting
Sklearn Gradient Boosting Operator
Home > Machine Learning > Sklearn
Input Properties
| Property | Requirement | Type | Default | Description |
|---|---|---|---|---|
| Target Attribute | ✓ | String | - | Attribute in your dataset corresponding to target |
| Count Vectorizer | Boolean | false | Convert a collection of text documents to a matrix of token counts | |
| ↳ Text Attribute | String | - | Attribute in your dataset with text to vectorize | |
| ↳ Tfidf Transformer | Boolean | false | Transform a count matrix to a normalized tf or tf-idf representation |
Output Ports
| Port | Mode |
|---|---|
| 0 | Set Snapshot |
1.12 - K-nearest Neighbors
Sklearn K-nearest Neighbors Operator
Home > Machine Learning > Sklearn
Input Properties
| Property | Requirement | Type | Default | Description |
|---|---|---|---|---|
| Target Attribute | ✓ | String | - | Attribute in your dataset corresponding to target |
| Count Vectorizer | Boolean | false | Convert a collection of text documents to a matrix of token counts | |
| ↳ Text Attribute | String | - | Attribute in your dataset with text to vectorize | |
| ↳ Tfidf Transformer | Boolean | false | Transform a count matrix to a normalized tf or tf-idf representation |
Output Ports
| Port | Mode |
|---|---|
| 0 | Set Snapshot |
1.13 - Linear Perceptron
Sklearn Linear Perceptron Operator
Home > Machine Learning > Sklearn
Input Properties
| Property | Requirement | Type | Default | Description |
|---|---|---|---|---|
| Target Attribute | ✓ | String | - | Attribute in your dataset corresponding to target |
| Count Vectorizer | Boolean | false | Convert a collection of text documents to a matrix of token counts | |
| ↳ Text Attribute | String | - | Attribute in your dataset with text to vectorize | |
| ↳ Tfidf Transformer | Boolean | false | Transform a count matrix to a normalized tf or tf-idf representation |
Output Ports
| Port | Mode |
|---|---|
| 0 | Set Snapshot |
1.14 - Linear Regression
Sklearn Linear Regression Operator
Home > Machine Learning > Sklearn
Input Properties
| Property | Requirement | Type | Default | Description |
|---|---|---|---|---|
| Target Attribute | ✓ | String | - | Attribute in your dataset corresponding to target |
| Degree | ✓ | Integer | 1 | Degree of polynomial function |
Output Ports
| Port | Mode |
|---|---|
| 0 | Set Snapshot |
1.15 - Linear Support Vector Machine
Sklearn Linear Support Vector Machine Operator
Home > Machine Learning > Sklearn
Input Properties
| Property | Requirement | Type | Default | Description |
|---|---|---|---|---|
| Target Attribute | ✓ | String | - | Attribute in your dataset corresponding to target |
| Count Vectorizer | Boolean | false | Convert a collection of text documents to a matrix of token counts | |
| ↳ Text Attribute | String | - | Attribute in your dataset with text to vectorize | |
| ↳ Tfidf Transformer | Boolean | false | Transform a count matrix to a normalized tf or tf-idf representation |
Output Ports
| Port | Mode |
|---|---|
| 0 | Set Snapshot |
1.16 - Logistic Regression
Sklearn Logistic Regression Operator
Home > Machine Learning > Sklearn
Input Properties
| Property | Requirement | Type | Default | Description |
|---|---|---|---|---|
| Target Attribute | ✓ | String | - | Attribute in your dataset corresponding to target |
| Count Vectorizer | Boolean | false | Convert a collection of text documents to a matrix of token counts | |
| ↳ Text Attribute | String | - | Attribute in your dataset with text to vectorize | |
| ↳ Tfidf Transformer | Boolean | false | Transform a count matrix to a normalized tf or tf-idf representation |
Output Ports
| Port | Mode |
|---|---|
| 0 | Set Snapshot |
1.17 - Logistic Regression Cross Validation
Sklearn Logistic Regression Cross Validation Operator
Home > Machine Learning > Sklearn
Input Properties
| Property | Requirement | Type | Default | Description |
|---|---|---|---|---|
| Target Attribute | ✓ | String | - | Attribute in your dataset corresponding to target |
| Count Vectorizer | Boolean | false | Convert a collection of text documents to a matrix of token counts | |
| ↳ Text Attribute | String | - | Attribute in your dataset with text to vectorize | |
| ↳ Tfidf Transformer | Boolean | false | Transform a count matrix to a normalized tf or tf-idf representation |
Output Ports
| Port | Mode |
|---|---|
| 0 | Set Snapshot |
1.18 - Multi-layer Perceptron
Sklearn Multi-layer Perceptron Operator
Home > Machine Learning > Sklearn
Input Properties
| Property | Requirement | Type | Default | Description |
|---|---|---|---|---|
| Target Attribute | ✓ | String | - | Attribute in your dataset corresponding to target |
| Count Vectorizer | Boolean | false | Convert a collection of text documents to a matrix of token counts | |
| ↳ Text Attribute | String | - | Attribute in your dataset with text to vectorize | |
| ↳ Tfidf Transformer | Boolean | false | Transform a count matrix to a normalized tf or tf-idf representation |
Output Ports
| Port | Mode |
|---|---|
| 0 | Set Snapshot |
1.19 - Multinomial Naive Bayes
Sklearn Multinomial Naive Bayes Operator
Home > Machine Learning > Sklearn
Input Properties
| Property | Requirement | Type | Default | Description |
|---|---|---|---|---|
| Target Attribute | ✓ | String | - | Attribute in your dataset corresponding to target |
| Count Vectorizer | Boolean | false | Convert a collection of text documents to a matrix of token counts | |
| ↳ Text Attribute | String | - | Attribute in your dataset with text to vectorize | |
| ↳ Tfidf Transformer | Boolean | false | Transform a count matrix to a normalized tf or tf-idf representation |
Output Ports
| Port | Mode |
|---|---|
| 0 | Set Snapshot |
1.20 - Nearest Centroid
Sklearn Nearest Centroid Operator
Home > Machine Learning > Sklearn
Input Properties
| Property | Requirement | Type | Default | Description |
|---|---|---|---|---|
| Target Attribute | ✓ | String | - | Attribute in your dataset corresponding to target |
| Count Vectorizer | Boolean | false | Convert a collection of text documents to a matrix of token counts | |
| ↳ Text Attribute | String | - | Attribute in your dataset with text to vectorize | |
| ↳ Tfidf Transformer | Boolean | false | Transform a count matrix to a normalized tf or tf-idf representation |
Output Ports
| Port | Mode |
|---|---|
| 0 | Set Snapshot |
1.21 - Passive Aggressive
Sklearn Passive Aggressive Operator
Home > Machine Learning > Sklearn
Input Properties
| Property | Requirement | Type | Default | Description |
|---|---|---|---|---|
| Target Attribute | ✓ | String | - | Attribute in your dataset corresponding to target |
| Count Vectorizer | Boolean | false | Convert a collection of text documents to a matrix of token counts | |
| ↳ Text Attribute | String | - | Attribute in your dataset with text to vectorize | |
| ↳ Tfidf Transformer | Boolean | false | Transform a count matrix to a normalized tf or tf-idf representation |
Output Ports
| Port | Mode |
|---|---|
| 0 | Set Snapshot |
1.22 - Probability Calibration
Sklearn Probability Calibration Operator
Home > Machine Learning > Sklearn
Input Properties
| Property | Requirement | Type | Default | Description |
|---|---|---|---|---|
| Target Attribute | ✓ | String | - | Attribute in your dataset corresponding to target |
| Count Vectorizer | Boolean | false | Convert a collection of text documents to a matrix of token counts | |
| ↳ Text Attribute | String | - | Attribute in your dataset with text to vectorize | |
| ↳ Tfidf Transformer | Boolean | false | Transform a count matrix to a normalized tf or tf-idf representation |
Output Ports
| Port | Mode |
|---|---|
| 0 | Set Snapshot |
1.23 - Random Forest
Sklearn Random Forest Operator
Home > Machine Learning > Sklearn
Input Properties
| Property | Requirement | Type | Default | Description |
|---|---|---|---|---|
| Target Attribute | ✓ | String | - | Attribute in your dataset corresponding to target |
| Count Vectorizer | Boolean | false | Convert a collection of text documents to a matrix of token counts | |
| ↳ Text Attribute | String | - | Attribute in your dataset with text to vectorize | |
| ↳ Tfidf Transformer | Boolean | false | Transform a count matrix to a normalized tf or tf-idf representation |
Output Ports
| Port | Mode |
|---|---|
| 0 | Set Snapshot |
1.24 - Ridge Regression
Sklearn Ridge Regression Operator
Home > Machine Learning > Sklearn
Input Properties
| Property | Requirement | Type | Default | Description |
|---|---|---|---|---|
| Target Attribute | ✓ | String | - | Attribute in your dataset corresponding to target |
| Count Vectorizer | Boolean | false | Convert a collection of text documents to a matrix of token counts | |
| ↳ Text Attribute | String | - | Attribute in your dataset with text to vectorize | |
| ↳ Tfidf Transformer | Boolean | false | Transform a count matrix to a normalized tf or tf-idf representation |
Output Ports
| Port | Mode |
|---|---|
| 0 | Set Snapshot |
1.25 - Ridge Regression Cross Validation
Sklearn Ridge Regression Cross Validation Operator
Home > Machine Learning > Sklearn
Input Properties
| Property | Requirement | Type | Default | Description |
|---|---|---|---|---|
| Target Attribute | ✓ | String | - | Attribute in your dataset corresponding to target |
| Count Vectorizer | Boolean | false | Convert a collection of text documents to a matrix of token counts | |
| ↳ Text Attribute | String | - | Attribute in your dataset with text to vectorize | |
| ↳ Tfidf Transformer | Boolean | false | Transform a count matrix to a normalized tf or tf-idf representation |
Output Ports
| Port | Mode |
|---|---|
| 0 | Set Snapshot |
1.26 - Sklearn Prediction
Sklearn Prediction Operator
Home > Machine Learning > Sklearn
Input Properties
| Property | Requirement | Type | Default | Description |
|---|---|---|---|---|
| Model Attribute | ✓ | String | model | Attribute corresponding to ML model |
| Output Attribute Name | ✓ | String | prediction | Attribute name of the prediction result |
| Ground Truth Attribute Name To Ignore | String | - | Attribute name of the ground truth |
Output Ports
| Port | Mode |
|---|---|
| 0 | Set Snapshot |
1.27 - Sklearn Testing
It will generate scorers for Sklearn model
Home > Machine Learning > Sklearn
Input Properties
| Property | Requirement | Type | Default | Description |
|---|---|---|---|---|
| Regression | ✓ | Boolean | false | Choose to solve a regression task |
| Model Attribute | ✓ | String | model | Attribute corresponding to ML model |
| Target Attribute | ✓ | String | - | Attribute in your dataset corresponding to target |
Output Ports
| Port | Mode |
|---|---|
| 0 | Set Snapshot |
1.28 - Stochastic Gradient Descent
Sklearn Stochastic Gradient Descent Operator
Home > Machine Learning > Sklearn
Input Properties
| Property | Requirement | Type | Default | Description |
|---|---|---|---|---|
| Target Attribute | ✓ | String | - | Attribute in your dataset corresponding to target |
| Count Vectorizer | Boolean | false | Convert a collection of text documents to a matrix of token counts | |
| ↳ Text Attribute | String | - | Attribute in your dataset with text to vectorize | |
| ↳ Tfidf Transformer | Boolean | false | Transform a count matrix to a normalized tf or tf-idf representation |
Output Ports
| Port | Mode |
|---|---|
| 0 | Set Snapshot |
1.29 - Support Vector Machine
Sklearn Support Vector Machine Operator
Home > Machine Learning > Sklearn
Input Properties
| Property | Requirement | Type | Default | Description |
|---|---|---|---|---|
| Target Attribute | ✓ | String | - | Attribute in your dataset corresponding to target |
| Count Vectorizer | Boolean | false | Convert a collection of text documents to a matrix of token counts | |
| ↳ Text Attribute | String | - | Attribute in your dataset with text to vectorize | |
| ↳ Tfidf Transformer | Boolean | false | Transform a count matrix to a normalized tf or tf-idf representation |
Output Ports
| Port | Mode |
|---|---|
| 0 | Set Snapshot |
2 - Advanced Sklearn
Operators in the Advanced Sklearn category
Home > Machine Learning > Advanced Sklearn
Operators
| Operator | Description |
|---|---|
| KNN Classifier | Sklearn KNN Classifier Operator |
| KNN Regressor | Sklearn KNN Regressor Operator |
| SVM Classifier | Sklearn SVM Classifier Operator |
| SVM Regressor | Sklearn SVM Regressor Operator |
Total: 4 operators
2.1 - KNN Classifier
Sklearn KNN Classifier Operator
Home > Machine Learning > Advanced Sklearn
Input Properties
| Property | Requirement | Type | Default | Description |
|---|---|---|---|---|
| Parameter Setting | ✓ | SklearnAdvancedKNNParameters | - | |
| Ground Truth Attribute Column | ✓ | String | - | Ground truth attribute column |
| Selected Features | ✓ | List | - | Features used to train the model |
Output Ports
| Port | Mode |
|---|---|
| 0 | Set Snapshot |
2.2 - KNN Regressor
Sklearn KNN Regressor Operator
Home > Machine Learning > Advanced Sklearn
Input Properties
| Property | Requirement | Type | Default | Description |
|---|---|---|---|---|
| Parameter Setting | ✓ | SklearnAdvancedKNNParameters | - | |
| Ground Truth Attribute Column | ✓ | String | - | Ground truth attribute column |
| Selected Features | ✓ | List | - | Features used to train the model |
Output Ports
| Port | Mode |
|---|---|
| 0 | Set Snapshot |
2.3 - SVM Classifier
Sklearn SVM Classifier Operator
Home > Machine Learning > Advanced Sklearn
Input Properties
| Property | Requirement | Type | Default | Description |
|---|---|---|---|---|
| Parameter Setting | ✓ | SklearnAdvancedSVCParameters | - | |
| Ground Truth Attribute Column | ✓ | String | - | Ground truth attribute column |
| Selected Features | ✓ | List | - | Features used to train the model |
Output Ports
| Port | Mode |
|---|---|
| 0 | Set Snapshot |
2.4 - SVM Regressor
Sklearn SVM Regressor Operator
Home > Machine Learning > Advanced Sklearn
Input Properties
| Property | Requirement | Type | Default | Description |
|---|---|---|---|---|
| Parameter Setting | ✓ | SklearnAdvancedSVRParameters | - | |
| Ground Truth Attribute Column | ✓ | String | - | Ground truth attribute column |
| Selected Features | ✓ | List | - | Features used to train the model |
Output Ports
| Port | Mode |
|---|---|
| 0 | Set Snapshot |
3 - Hugging Face
Operators in the Hugging Face category
Home > Machine Learning > Hugging Face
Operators
| Operator | Description |
|---|---|
| Hugging Face Iris Logistic Regression | Predict whether an iris is an Iris-setosa using a pre-trained logistic regression model |
| Hugging Face Sentiment Analysis | Analyzing Sentiments with a Twitter-Based Model from Hugging Face |
| Hugging Face Spam Detection | Spam Detection by SMS Spam Detection Model from Hugging Face |
| Hugging Face Text Summarization | Summarize the given text content with a mini2bert pre-trained model from Hugging Face |
Total: 4 operators
3.1 - Hugging Face Iris Logistic Regression
Predict whether an iris is an Iris-setosa using a pre-trained logistic regression model
Home > Machine Learning > Hugging Face
Input Properties
| Property | Requirement | Type | Default | Description |
|---|---|---|---|---|
| Petal Length Cm Attribute | ✓ | String | - | Attribute in your dataset corresponding to PetalLengthCm |
| Petal Width Cm Attribute | ✓ | String | - | Attribute in your dataset corresponding to PetalWidthCm |
| Prediction Class Name | ✓ | String | Species_prediction | Output attribute name for the predicted class of species |
| Prediction Probability Name | ✓ | String | Species_probability | Output attribute name for the prediction’s probability of being a Iris-setosa |
Output Ports
| Port | Mode |
|---|---|
| 0 | Set Snapshot |
3.2 - Hugging Face Sentiment Analysis
Analyzing Sentiments with a Twitter-Based Model from Hugging Face
Home > Machine Learning > Hugging Face
Input Properties
| Property | Requirement | Type | Default | Description |
|---|---|---|---|---|
| Attribute | ✓ | String | - | Column to perform sentiment analysis on |
| Positive Result Attribute | ✓ | String | huggingface_sentiment_positive | Column name of the sentiment analysis result (positive) |
| Neutral Result Attribute | ✓ | String | huggingface_sentiment_neutral | Column name of the sentiment analysis result (neutral) |
| Negative Result Attribute | ✓ | String | huggingface_sentiment_negative | Column name of the sentiment analysis result (negative) |
Output Ports
| Port | Mode |
|---|---|
| 0 | Set Snapshot |
3.3 - Hugging Face Spam Detection
Spam Detection by SMS Spam Detection Model from Hugging Face
Home > Machine Learning > Hugging Face
Input Properties
| Property | Requirement | Type | Default | Description |
|---|---|---|---|---|
| Attribute | ✓ | String | - | Column to perform spam detection on |
| Spam Result Attribute | ✓ | String | is_spam | Column name of whether spam or not |
| Score Result Attribute | ✓ | String | score | Column name of Probability for classification |
Output Ports
| Port | Mode |
|---|---|
| 0 | Set Snapshot |
3.4 - Hugging Face Text Summarization
Summarize the given text content with a mini2bert pre-trained model from Hugging Face
Home > Machine Learning > Hugging Face
Input Properties
| Property | Requirement | Type | Default | Description |
|---|---|---|---|---|
| Attribute | ✓ | String | - | Attribute to perform text summarization on |
| Result Attribute Name | String | summary | Attribute name of the text summary result |
Output Ports
| Port | Mode |
|---|---|
| 0 | Set Snapshot |
4 - Machine Learning General
Operators in the Machine Learning General category
Home > Machine Learning > Machine Learning General
Operators
| Operator | Description |
|---|---|
| Machine Learning Scorer | Scorer for machine learning models |
Total: 1 operator
4.1 - Machine Learning Scorer
Scorer for machine learning models
Home > Machine Learning > Machine Learning General
Input Properties
| Property | Requirement | Type | Default | Description |
|---|---|---|---|---|
| Regression | ✓ | Boolean | false | Choose to solve a regression task |
| ↳ Scorer Functions | List | - | Select classification tasks metrics | |
| ↳ Scorer Functions | List | - | Select regression tasks metrics | |
| Actual Value | ✓ | String | - | Specify the label attribute |
| Predicted Value | ✓ | String | - | Specify the attribute generated by the model |
Output Ports
| Port | Mode |
|---|---|
| 0 | Set Snapshot |