Classification with PyCaret
Learn how to import necessary libraries and datasets for classification with PyCaret.
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Classification is one of the fundamental supervised learning tasks. Its goal is to predict a categorical variable known as the class label. This task is known as binary classification when there are only two classes ( and ), or multiclass classification in case there are more. One of the most widely used binary classification models is logistic regression. It is defined in the following equation:
- is the natural logarithm of the odds, known as the logit function.
- to are the feature variables.
- is the intercept term.
- to are the coefficients of the feature variables.
- is the vectorized form of the equation. Our goal is to calculate which is the probability that an instance of the given dataset belongs to class . The logistic function is the inverse of the logit (or log-odds) function, so we can apply it and get the desired result.
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