Exploring Machine Learning Concepts

What is the main goal of supervised learning?

• To classify data without any labels.

• To reduce the amount of data used for training.

• To train a model to make predictions based on labeled data.

• To create unsupervised models for clustering.

Which of the following is a common algorithm used in supervised learning?

• Decision Tree

• K-Means Clustering

• Linear Regression

• Support Vector Machine

What is the difference between classification and regression in supervised learning?

• Classification predicts future values; regression predicts past values.

• Classification uses numerical data; regression uses text data.

• Classification is used for time series analysis; regression is used for clustering.

• Classification deals with categorical outcomes; regression deals with continuous outcomes.

In unsupervised learning, what is the primary objective?

• To reduce the dimensionality of labeled datasets.

• To classify data into predefined categories.

• To identify patterns or groupings in data without prior labels.

• To predict future outcomes based on historical data.

Which of the following techniques is commonly used in unsupervised learning?

• Regression

• Dimensionality Reduction

• Classification

• Clustering

What is clustering in the context of unsupervised learning?

• Clustering involves labeling data points with predefined categories.

• Clustering is the process of sorting data points in ascending order.

• Clustering is a method for supervised learning.

• Clustering is the process of grouping similar data points in unsupervised learning.

How does a Support Vector Machine (SVM) work?

• SVM uses decision trees to classify data.

• SVM relies on clustering techniques to group similar data points.

• A Support Vector Machine (SVM) finds the optimal hyperplane that separates different classes by maximizing the margin between support vectors.

• SVM minimizes the distance between all data points to find a solution.

What is the purpose of the kernel trick in SVM?

• To reduce the dimensionality of the data before classification.

• To enable SVM to classify data in higher-dimensional spaces without explicit computation.

• To improve the speed of SVM training by simplifying the data.

• To visualize the data in a two-dimensional space for easier interpretation.

What are the advantages of using SVM for classification tasks?

• SVM requires a large amount of labeled data to function.

• SVM cannot handle multi-class classification problems.

• SVM is only effective for linear data.

• Advantages of SVM include effective handling of high-dimensional data, robustness to overfitting, ability to use kernel functions for non-linear classification, and effectiveness in cases with fewer samples than dimensions.

What is overfitting in the context of supervised learning?

• Overfitting is when a model learns the training data too well, leading to poor performance on new data.

• Overfitting happens when a model is trained on too little data, leading to generalization issues.

• Overfitting occurs when a model is too simple and cannot capture the underlying patterns.

• Overfitting is when a model performs equally well on both training and new data.