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08_​Regularized_​Logistic_​Regression

Impact of Regularization in case of Logistic Regression

The goal of this workflow is to analyze the impact of different priors in case of the logistic regression. The workflow therefore first reads the internet advertisement dataset. Then it creates a subset with more columns than rows, favouring overfitting. In the next step three models with different prior options are trained. In the last step the results are summarized in an interactive javascript view.

Data Reading and Preprocessing Generation of Training and Test Set Model Training with different Regularization Settings Without Regularization Laplace Regularization Gauss Regularization Coefficients and Performance Evaluation Impact of Regularization in case of Logistic RegressionThe goal of this workflow is to analyze the impact of different priors in case of the logistic regression. The workflow therefore first reads the internet advertisement dataset. Then it creates a subset with more columns than rows, favouring overfitting. In the next step three models with different prior options are trained. In thelast step the results are summarized in an interactive javascript view. LaplaceGaussUniformWith more columns than rows LogisticRegression Learner LogisticRegression Learner Logistic RegressionPredictor Logistic RegressionPredictor Logistic RegressionPredictor LogisticRegression Learner Read andPreprocess Data Join Coefficients Join Predictions Delete ConstantColumns Calculate andExtract Accuracy Visualize Results Create Trainingand Test Set Data Reading and Preprocessing Generation of Training and Test Set Model Training with different Regularization Settings Without Regularization Laplace Regularization Gauss Regularization Coefficients and Performance Evaluation Impact of Regularization in case of Logistic RegressionThe goal of this workflow is to analyze the impact of different priors in case of the logistic regression. The workflow therefore first reads the internet advertisement dataset. Then it creates a subset with more columns than rows, favouring overfitting. In the next step three models with different prior options are trained. In thelast step the results are summarized in an interactive javascript view. LaplaceGaussUniformWith more columns than rows LogisticRegression Learner LogisticRegression Learner Logistic RegressionPredictor Logistic RegressionPredictor Logistic RegressionPredictor LogisticRegression Learner Read andPreprocess Data Join Coefficients Join Predictions Delete ConstantColumns Calculate andExtract Accuracy Visualize Results Create Trainingand Test Set

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