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Sklearn

Operators in the Sklearn category

1: Sklearn Training

1.1: Training: Adaptive Boosting
1.2: Training: Bagging Training
1.3: Training: Bernoulli Naive Bayes
1.4: Training: Complement Naive Bayes
1.5: Training: Decision Tree
1.6: Training: Dummy Classifier
1.7: Training: Extra Tree
1.8: Training: Extra Trees
1.9: Training: Gaussian Naive Bayes
1.10: Training: Gradient Boosting
1.11: Training: K-nearest Neighbors
1.12: Training: Linear Perceptron
1.13: Training: Linear Regression
1.14: Training: Linear Support Vector Machine
1.15: Training: Logistic Regression
1.16: Training: Logistic Regression Cross Validation
1.17: Training: Multi-layer Perceptron
1.18: Training: Multinomial Naive Bayes
1.19: Training: Nearest Centroid
1.20: Training: Passive Aggressive
1.21: Training: Probability Calibration
1.22: Training: Random Forest
1.23: Training: Ridge Regression
1.24: Training: Ridge Regression Cross Validation
1.25: Training: Stochastic Gradient Descent
1.26: Training: Support Vector Machine

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

Home > Machine Learning > Sklearn

Subcategories

Sklearn Training

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 - 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 - 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

1.2 - 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

1.3 - 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

1.4 - 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

1.5 - 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

1.6 - 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

1.7 - 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

1.8 - 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

1.9 - 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

1.10 - 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

1.11 - 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

1.12 - 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

1.13 - 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

1.14 - 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

1.15 - 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

1.16 - 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

1.17 - 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

1.18 - 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

1.19 - 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

1.20 - 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

1.21 - 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

1.22 - 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

1.23 - 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

1.24 - 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

1.25 - 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

1.26 - 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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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