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Why is logistic regression good for classification?
Logistic regression is a simple yet very effective classification algorithm so it is commonly used for many binary classification tasks. Logistic regression model takes a linear equation as input and use logistic function and log odds to perform a binary classification task.
Why do we calibrate models?
We calibrate our model when the probability estimate of a data point belonging to a class is very important. Calibration is comparison of the actual output and the expected output given by a system. Now let me put this in the perspective of machine learning.
How are probabilities of a logistic regression model calibrated?
In general, scores returned by machine learning models are not necessarily well-calibrated probabilities (see my post on ROC space and AUC). The probability estimates from a logistic regression model (without regularization) are partially calibrated, though.
Are there any generalized linear models that are calibrated?
The probability estimates from a logistic regression model (without regularization) are partially calibrated, though. In fact, many generalized linear models, including linear regression, logistic regression, binomial regression, and Poisson regression, give calibrated predicted values.
What happens when you limit the capacity of a logistic regression?
Thus, limiting the model capacity (fewer features for instance) can hinder the calibration of a logistic regression by increasing the distance between the best learnable model and the true model. Using an incorrect observation model with the logistic regression will lead to uncalibrated probabilities.
Which is the steepest part of the logistic curve?
Since the red line is the steepest part of the logistic curve, the approximated change \\(\\frac{\\beta_j}{4}\\) is always an upper bound (even for probabilities outsidethe range 0.3 to 0.7). Fitting logistic regression and calibration Logistic regression is fit with maximum likelihood estimation.