**Logistic Regression**

Logistic Regression is a classification algorithm. It is used to predict a binary outcome (1 / 0, Yes / No, True / False) given a set of independent variables. To represent binary / categorical outcome, we use dummy variables.

You can also think of logistic regression as a special case of linear regression when the outcome variable is categorical, where we are using log of odds as dependent variable. In simple words, it predicts the probability of occurrence of an event by fitting data to a logit function.

Derivation of Logistic Regression Equation

In logistic regression, the dependent variable is a logit, which is the natural log of the odds, that is,

So a logit is a log of odds and odds are a function of P. In logistic regression, we find

**logit(P) = a + bX,**

This is the equation used in Logistic Regression. Here (p/1-p) is the odd ratio. Whenever the log of odd ratio is found to be positive, the probability of success is always more than 50%. A typical logistic model plot is shown below. You can see probability never goes below 0 and above 1.

**Important Points**

Logistic Regression is part of a larger class of algorithms known as Generalized Linear Model (glm).

GLM does not assume a linear relationship between dependent and independent variables. However, it assumes a linear relationship between link function and independent variables in logit model.

The dependent variable need not to be normally distributed.

It does not use OLS (Ordinary Least Square) for parameter estimation. Instead, it uses maximum likelihood estimation (MLE).

Errors need to be independent but not normally distributed.

**Performance of Logistic Regression Model**

**AIC (Akaike Information Criteria**) – The analogous metric of adjusted R² in logistic regression is AIC. AIC is the measure of fit which penalizes model for the number of model coefficients. Therefore, we always prefer model with minimum AIC value

**Null Deviance and Residual Deviance**– Null Deviance indicates the response predicted by a model with nothing but an intercept. Lower the value, better the model. Residual deviance indicates the response predicted by a model on adding independent variables. Lower the value, better the model.

**Confusion Matrix:** It is nothing but a tabular representation of Actual vs Predicted values. This helps us to find the accuracy of the model and avoid overfitting. This is how it looks like:

You can calculate the **accuracy** of your model with:

From confusion matrix, Specificity and Sensitivity can be derived as illustrated below:

Specificity and Sensitivity plays a crucial role in deriving ROC curve.

**ROC Curve:** Receiver Operating Characteristic(ROC) summarizes the model’s performance by evaluating the trade-offs between true positive rate (sensitivity) and false positive rate (1- specificity).

For plotting ROC, it is advisable to assume p > 0.5 since we are more concerned about success rate. ROC summarizes the predictive power for all possible values of p > 0.5. The area under curve (AUC), referred to as index of accuracy(A) or concordance index, is a perfect performance metric for ROC curve. Higher the area under curve, better the prediction power of the model. Below is a sample ROC curve. The ROC of a perfect predictive model has TP equals 1 and FP equals 0. This curve will touch the top left corner of the graph.

Note: For model performance, you can also consider likelihood function. It is called so, because it selects the coefficient values which maximizes the likelihood of explaining the observed data. It indicates goodness of fit as its value approaches one, and a poor fit of the data as its value approaches zero.

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