Basics of Classification Models

Statistical model.

Classification is a type of supervised learning where the outcome (target variable) is categorical. It involves training a model to predict or categorize the class labels of the target variable based on the input features. Some of the common applications of classification models include email spam detection, customer churn prediction, and disease diagnosis.

Logistic Regression

Logistic Regression is a classification algorithm used when the response variable is categorical. Unlike linear regression, which uses a straight line to model the relationship between variables, logistic regression uses the logistic function to model the probability of a certain class or event.

The logistic function, also known as the sigmoid function, can take any real-valued number and map it into a value between 0 and 1. This makes it suitable for modeling the probability of a binary outcome.

Decision Trees

Decision Trees are a type of flowchart-like structure in which each internal node represents a feature (or attribute), each branch represents a decision rule, and each leaf node represents an outcome. The topmost node in a decision tree is known as the root node.

Decision trees are simple to understand and interpret, and they can handle both categorical and numerical data. However, they can easily overfit the data if not properly pruned.

Understanding Confusion Matrix

A confusion matrix is a table that is often used to describe the performance of a classification model on a set of data for which the true values are known. It contains information about actual and predicted classifications done by a classification system.

The four terms used in confusion matrix are:

• True Positives (TP): The cases in which we predicted YES and the actual output was also YES.
• True Negatives (TN): The cases in which we predicted NO and the actual output was NO.
• False Positives (FP): The cases in which we predicted YES and the actual output was NO.
• False Negatives (FN): The cases in which we predicted NO and the actual output was YES.

Evaluation Metrics for Classification Models

There are several metrics used to evaluate the performance of classification models, including:

• Accuracy: It is the ratio of the number of correct predictions to the total number of input samples.
• Precision: It is the ratio of the number of true positives to the sum of true positives and false positives. It shows how precise your model is out of those predicted positive, how many of them are actual positive.
• Recall (Sensitivity): It is the ratio of the number of true positives to the sum of true positives and false negatives. It shows how many of the actual positives your model is able to capture.
• F1-Score: It is the harmonic mean of Precision and Recall. It tries to find the balance between precision and recall.

By understanding these basics of classification models, you can start to apply these techniques to your own data and begin to see the power of machine learning in action.