6. Classifier

1. Click on the Classifier under the Machine Learning category.

2. Model Type: Select the Model Type of the classifier you want to use:

   • Logistic Regression

   • BernoulliNB

   • MultinomialNB

   • GaussianNB

   • SVC(SupportVectorMachine Classifier)

   • DecisionTree Classifier

   • RandomForest Classifier

   • GradientBoosting Classifier

   • XGB Classifier

   • LGBM Classifier

   • CatBoost Classifier

3. Allocate to: Specify the variable name to assign to the model.

4. Code View: Preview the generated code.

5. Run: Execute the code.

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Logistic Regression

1. Penalty: Specify the regularization method for the model. (l2 / l1 / elasticnet / none)

2. C: Adjust the regularization strength.

3. Random State: Set the seed value for the random number generator.

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SupportVectorMachine Classifier

1. C: C indicates the freedom of the model's regularization. A higher C value makes the model more complex to fit the training data.

2. Kernel: A function that maps data into higher dimensions. You can control the complexity of the model by selecting the kernel type.

   • Degree (Poly): Degree determines the degree of the polynomial. A higher degree increases the complexity of the model.

   • Gamma (Poly, rbf, sigmoid): Gamma adjusts the curvature of the decision boundary. A higher value makes the model fit the training data more closely.

   • Coef0 (Poly, sigmoid): An additional parameter for the kernel, controlling the offset of the kernel. A higher value makes the model fit the training data more closely.

3. Random State: Set the seed value for the random number generator.

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DecisionTree Classifier

1. Criterion: Specify the metric used to select the node split. (squared_error / friedman_mse / absolute_error / Poisson)

2. Max Depth: Specify the maximum depth of the trees.

3. Min Samples Split: Specify the minimum number of samples required to split a node to prevent excessive splitting.

4. Random State: Set the seed value for the random number generator.

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RandomForest Classifier

1. N estimators: Specify the number of trees to include in the ensemble.

2. Criterion: Specify the metric used to select the node split. Options include gini / entropy.

3. Max Depth: Specify the maximum depth of the trees.

4. Min Samples Split: Specify the minimum number of samples required to split a node to prevent excessive splitting.

5. N jobs: Specify the number of CPU cores or threads to use during model training for parallel processing.

6. Random State: Set the seed value for the random number generator.

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GradientBoosting Classifier

1. Loss: Specify the loss function to be used. Options include deviance / exponential.

2. Learning rate: Adjust the contribution of each tree and the degree to which the errors of previous trees are corrected. A large value may lead to non-convergence or overfitting, while a small value may increase training time.

3. N estimators: Specify the number of trees to include in the ensemble.

4. Criterion: Specify the metric used to select the node split. (friedman_mse / squared_error / mse / mae)

5. Random State: Set the seed value for the random number generator.

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XGB Classifier

1. N estimators: Specify the number of trees to include in the ensemble.

2. Max Depth: Specify the maximum depth of the trees.

3. Learning Rate: Adjust the contribution of each tree and the degree to which the errors of previous trees are corrected.

4. Gamma: Adjust the curvature of the decision boundary. A higher value makes the model fit the training data more closely.

5. Random State: Set the seed value for the random number generator.

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LGBM Classifier

1. Boosting type: Specify the boosting method used internally in the algorithm. (gbdt / dart / goss / rf (Random Forest))

2. Max Depth: Specify the maximum depth of the trees.

3. Learning rate: Adjust the contribution of each tree and the degree to which the errors of previous trees are corrected.

4. N estimators: Specify the number of trees to include in the ensemble.

5. Random State: Set the seed value for the random number generator.

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CatBoost Classifier

1. Learning rate: Adjust the contribution of each tree and the degree to which the errors of previous trees are corrected.

2. Loss function: Specify the loss function to be used. (RMSE / absolute_error / huber / quantile)

3. Task type: Specify the hardware used for data processing. (CPU / GPU)

4. Max depth: Specify the maximum depth of the trees.

5. N estimators: Specify the number of trees to include in the ensemble.

6. Random state: Set the seed value for the random number generator.