### What is Logistic Regression?

Logistic regression is a supervised learning algorithm that is used to predict the probability of categorical dependent variable.

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Logistic regression is a supervised learning algorithm that is used to predict the probability of categorical dependent variable.

Logistic regression uses a logistic loss function, where the cost for a single observation is represented by:

Key assumptions of logistic regression are independence of observations; linear relationship between independent variables and the log odds of the dependent variable

Advantages: Simple and easy to understand, efficient, interpretable. Disadvantages: Linearity assumption, sensitive to outliers, overfitting

The equivalent of the overall F-Test in logistic regression is Deviance.

The least squares cost function is non-convex in a binary classification setting, meaning the algorithm could get stuck in a local rather than global minimum and thus fail to optimize the loss.

Each ? is interpreted as the change in log odds of a success for a 1-unit increase in the corresponding predictor, holding all other variables constant.

Logistic regression relates the log odds to the weighted combination of predictors and coefficients

The odds, or the ratio of the probability of success to that of failure, can only take on positive values.

Predicted values would not be constrained to the range of [0,1], resulting in predictions that are not valid probabilities.

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- Machine Learning 101 (30)
- Statistics 101 (38)
- Supervised Learning (113)
- Unsupervised Learning (55)
- Deep Learning (23)
- Data Preparation (37)
- General (7)
- Standardization (6)
- Missing data (7)
- Textual Data (17)