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In Linear Regression, what is the objective of the algorithm?
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To minimize the error between the observed and predicted values.
How does a Decision Tree split the data?
Recursively, based on feature values using impurity measures like Gini index and Information Gain.
Describe the difference between K-means and Hierarchical Clustering.
K-means forms exclusive clusters, while Hierarchical clustering creates a tree-like structure of clusters, often allowing for overlapping clusters.
What does the K-Nearest Neighbor (KNN) algorithm use to classify new data points?
Similarity measures such as Euclidean distance and majority voting from the nearest neighbors.
Explain the concept behind Random Forest.
Random Forest is an ensemble method that uses multiple decision trees to improve accuracy through techniques like majority voting or averaging regression output.
What are some practical applications of data science?
Forecasting sales, market analysis, detecting fraud, recommendation systems, autonomous robots, image recognition, and natural language processing.
What are the primary responsibilities of a data scientist?
Data extraction, cleaning, exploration, modeling, and evaluation.
Why is there an increasing need for data science?
Due to the massive increase in data generation (e.g., 2.5 quintillion bytes/day) and the necessity to process and derive actionable insights for better business decisions.
What is the key objective of a Support Vector Machine (SVM)?
To find the hyperplane that best separates data into classes, maximizing the margin between support vectors.
What does the Apriori Algorithm identify in Association Rule Mining?
Frequent itemsets and generates association rules based on metrics like Support, Confidence, and Lift.
What are the core components of Reinforcement Learning?
Agent, Environment, Action, State, Reward, Policy, Value, and Action Value (Q-value).
What is the function used in Logistic Regression to map predicted values to a category?
Sigmoid function.
What assumption does Naive Bayes make about features?
It assumes that features are independent.
How does Q-Learning help in Reinforcement Learning?
It helps the agent to learn the value of actions and states based on reward maximization and exploration.
What type of neural network is commonly used for image recognition?
Convolutional Neural Networks (CNN).
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