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What is the target variable in logistic regression?
Logistic regression can be used only with two types of target variables: A categorical target variable that has exactly two categories (i.e., a binary or dichotomous variable). A continuous target variable that has values in the range 0.0 to 1.0 representing probability values or proportions.
How do you optimize a logistic regression model?
How to optimize hyper parameters of a Logistic Regression model using Grid Search in Python?
- Hyper-parameters of logistic regression.
- Implements Standard Scaler function on the dataset.
- Performs train_test_split on your dataset.
- Uses Cross Validation to prevent overfitting.
Why is target encoding bad for categorical data?
Target encoding introduces noise into the encoding of the categorical variables (noise which comes from the noise in the target variable itself). Also, naively applying target encoding can allow data leakage, leading to overfitting and poor predictive performance.
How is target encoding different from label encoding?
Target encoding allows us to retain actual useful information about the categories (like one-hot encoding, but unlike label encoding), while keeping the dimensionality of our data the same as the unencoded data (like label encoding, but unlike one-hot encoding).
How is the logit transformation used in logistic regression?
The Logit transformation is simply the log of the odds. So by using the WOE-tranformed predictors in logistic regression, the predictors are coded to the same WOE scale, and the parameters in the linear logistic regression equation can be directly compared.
Why are random values used in targeting encoding?
Targeting encoding can be seriously plagued by overfitting because it uses information about the target, called target leakage. To overcome this issue, you can add tiny random values to the new variable in order not to be fit too precisely for the training data. You may ask how tiny is tiny?