# Supervised ML: Classification (Data Science & ML)

> Logistic regression, KNN, SVM, metrics (accuracy, precision, recall, F1, ROC-AUC), thresholds

- 24 interview questions
- Mid-Level
- [Interview Questions: Data Science & ML](https://sharpskill.dev/en/technologies/data-science/interview-questions.md)

## 1. What is the main objective of a supervised classification algorithm?

**Answer**

Supervised classification aims to predict a category or class (discrete variable) from input features, by learning from labeled data. Unlike regression which predicts continuous values, classification assigns each observation to a predefined class (binary or multiclass).

## 2. Which mathematical function does logistic regression use to transform predictions into probabilities?

**Answer**

The sigmoid (or logistic) function transforms any real value into a probability between 0 and 1. It is defined as sigma(z) = 1/(1+e^(-z)). This function allows interpreting the output as the probability of belonging to the positive class.

## 3. What do the coefficients represent in a logistic regression model?

**Answer**

Logistic regression coefficients represent the change in log-odds for each unit change in the corresponding feature. A positive coefficient increases the probability of the positive class, while a negative coefficient decreases it. The exponential of the coefficient gives the odds ratio.

## 21 more questions available

- How does the K-Nearest Neighbors (KNN) algorithm work for classification?
- What is the impact of choosing the value of k in the KNN algorithm?

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