Machine Learning Interview Questions – Part 2
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01. What is Linear Regression?
Linear Regression is a supervised machine learning algorithm used to predict a continuous numerical value by modeling the relationship between input variables and an output.
Key Points
- Used for numerical prediction
- Models linear relationships
- Example: predicting house prices
02. What is Logistic Regression?
Logistic Regression is a supervised learning algorithm commonly used for classification. It estimates the probability of an input belonging to a class.
Key Points
- Commonly used for binary classification
- Uses a logistic function in its standard form
- Example: spam or not-spam prediction
03. What is a Decision Tree?
A Decision Tree is a machine learning model that makes predictions using a sequence of feature-based decisions represented as branches.
Key Points
- Easy to interpret
- Supports classification and regression
- Can overfit without suitable constraints
04. What is Random Forest?
Random Forest is an ensemble learning algorithm that combines predictions from multiple decision trees to improve performance and reduce overfitting compared with a single unconstrained tree in many cases.
Key Points
- Uses multiple decision trees
- Supports classification and regression
- Combines predictions from individual trees
05. What is K-Nearest Neighbors (KNN)?
KNN is a supervised learning algorithm that predicts a class or numerical value using nearby training examples.
Key Points
- Uses a distance measure
- Classification often uses majority voting
- Performance depends on the choice of K and feature scaling
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01. What is Linear Regression?
Linear Regression is a supervised machine learning algorithm used to predict a continuous numerical value by modeling the relationship between input variables and an output.
Key Points
- Used for numerical prediction
- Models linear relationships
- Example: predicting house prices
02. What is Logistic Regression?
Logistic Regression is a supervised learning algorithm commonly used for classification. It estimates the probability of an input belonging to a class.
Key Points
- Commonly used for binary classification
- Uses a logistic function in its standard form
- Example: spam or not-spam prediction
03. What is a Decision Tree?
A Decision Tree is a machine learning model that makes predictions using a sequence of feature-based decisions represented as branches.
Key Points
- Easy to interpret
- Supports classification and regression
- Can overfit without suitable constraints
04. What is Random Forest?
Random Forest is an ensemble learning algorithm that combines predictions from multiple decision trees to improve performance and reduce overfitting compared with a single unconstrained tree in many cases.
Key Points
- Uses multiple decision trees
- Supports classification and regression
- Combines predictions from individual trees
05. What is K-Nearest Neighbors (KNN)?
KNN is a supervised learning algorithm that predicts a class or numerical value using nearby training examples.
Key Points
- Uses a distance measure
- Classification often uses majority voting
- Performance depends on the choice of K and feature scaling
━━━━━━━━━━━━━━━━━━━━━━
UPDATEGADH | Technology Tutorials & Interview Questions
#MachineLearning #MLInterviewQuestions #ArtificialIntelligence #DataScience #Python #CodingInterview #UPDATEGADH