Logistic Regression II
Lesson 1 of 1
  1. 1
    Assumptions of Logistic Regression I
    We’re now ready to delve deeper into Logistic Regression! In this lesson, we will cover the different assumptions that go into logistic regression, model hyperparameters, how to evaluate a classifi…
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  2. 2
    Assumptions of Logistic Regression II
    ### 1. Features linearly related to log odds Similar to linear regression, the underlying assumption of logistic regression is that the features are linearly related to the logit of the outcome. T…
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  3. 3
    Logistic Regression Parameters in `scikit-learn`
    #### Model Training and Hyperparameters Now that we have checked the assumptions of Logistic Regresion, we can eliminate the appropriate features and train and predict a model using scikit-learn. …
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  4. 4
    Prediction Thresholds
    Logistic regression not only predicts the class of a sample, but also the probability of a sample belonging to each class. In this way, a measure of certainty is associated to each prediction. In…
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  5. 5
    ROC Curve and AUC
    We have examined how changing the threshold can affect the logistic regression predictions, without retraining or changing the coefficients of the model. In essence, there is a continuum of predic…
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  6. 6
    Class Imbalance
    Class imbalance is when your binary classes are not evenly split. Technically, anything different from a 50/50 distribution would be imbalanced and need appropriate care. However, the more imbala…
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