Logistic Regression II
Lesson 1 of 1
  1. 1
    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…
  2. 2
    ### 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…
  3. 3
    #### 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. …
  4. 4
    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…
  5. 5
    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…
  6. 6
    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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