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Sklearn Training

Operators in the Sklearn Training category

1: Training: Adaptive Boosting
2: Training: Bagging Training
3: Training: Bernoulli Naive Bayes
4: Training: Complement Naive Bayes
5: Training: Decision Tree
6: Training: Dummy Classifier
7: Training: Extra Tree
8: Training: Extra Trees
9: Training: Gaussian Naive Bayes
10: Training: Gradient Boosting
11: Training: K-nearest Neighbors
12: Training: Linear Perceptron
13: Training: Linear Regression
14: Training: Linear Support Vector Machine
15: Training: Logistic Regression
16: Training: Logistic Regression Cross Validation
17: Training: Multi-layer Perceptron
18: Training: Multinomial Naive Bayes
19: Training: Nearest Centroid
20: Training: Passive Aggressive
21: Training: Probability Calibration
22: Training: Random Forest
23: Training: Ridge Regression
24: Training: Ridge Regression Cross Validation
25: Training: Stochastic Gradient Descent
26: Training: Support Vector Machine

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 - 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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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