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Scikit-Learn provides a simple way to apply Logistic Regression to your data. It fits a model to your training data and can then predict which category new or unseen data points belong to. You can customize important settings like the method for penalizing errors and the algorithm used for finding the best model.
In Logistic Regression, the sigmoid function helps convert predictions into probabilities between 0 and 1. This makes it easy to decide whether a data point belongs to one class or another. The sigmoid function is popular in machine learning because it provides clear probabilities and its math is straightforward.
A Classification Threshold is a value that decides how to turn the probability from the Logistic Regression model into a final decision. For example, if you set the threshold at 0.5, a data point with a probability greater than 0.5 is classified as one class, while anything less is classified as the other. Adjusting this threshold can help improve your model's performance for specific problems.
Logistic Regression is relatively easy to interpret because it shows how each feature (like age or income) affects the outcome. Each feature has a coefficient that indicates its impact, which helps us understand which features are most important.
In Logistic Regression, Log-Odds are used to express the likelihood of a data point belonging to a particular class. They are calculated by multiplying the feature values by their coefficients. Although Log-Odds themselves can be any real number, they help in calculating the probability of the outcome.
Logistic Regression is mainly used to predict which of two possible outcomes will happen. For example, it can predict whether an email is spam or not. It can also be extended to handle more than two outcomes by creating multiple models to compare different classes.
Logistic Regression predicts the likelihood of a data point falling into a certain category by drawing a line (or boundary) that separates different categories. When making a prediction, it determines which side of the boundary the data point falls on to decide its class.
To measure how well a Logistic Regression model performs, we use something called Log Loss (or Cross Entropy Loss). This measures how close the model's predictions are to the actual outcomes. Lower Log Loss means the model is making more accurate predictions.
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