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7.Overfitting and underfitting
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1.Introduction to regression
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2.Importing data for supervised learning
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3.The basics of linear regression
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4.Fit & predict for regression
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5.Train test split for regression
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6.Cross-validation
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7.5-fold cross-validation
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8.K-Fold CV comparison
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9.Regularized regression
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10.Regularization I Lasso
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11.Regularization II Ridge
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1.How good is your model
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2.Metrics for classification
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3.Logistic regression and the ROC curve
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4.Building a logistic regression model
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5.Plotting an ROC curve
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6.Area under the ROC curve
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7.AUC computation