Exploring Machine Learning Concepts

What is the main goal of supervised learning?

• To learn a mapping from inputs to outputs using labeled data.

• To optimize the performance of unsupervised learning.

• To classify data without any labels.

• To generate new data points from existing data.

Which algorithm is commonly used for clustering in unsupervised learning?

• Linear Regression

• Decision Trees

• K-means

• Support Vector Machines

What is a neural network primarily used for?

• Data storage and retrieval

• Neural networks are primarily used for machine learning tasks such as classification and regression.

• Web development and design

• Image editing and manipulation

How does a decision tree make decisions?

• A decision tree makes decisions by averaging all feature values to find a midpoint.

• A decision tree makes decisions by randomly selecting features without any criteria.

• A decision tree makes decisions by using a single feature to classify all data points.

• A decision tree makes decisions by recursively splitting data based on feature values until reaching a final prediction.

What is the purpose of a support vector machine?

• To cluster similar data points into groups.

• The purpose of a support vector machine is to classify data by finding the optimal hyperplane that separates different classes.

• To perform regression analysis on time series data.

• To reduce the dimensionality of data for visualization.

What metric is commonly used to evaluate classification models?

• Precision

• Recall

• F1 Score

• Accuracy

What is overfitting in the context of machine learning?

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

• Overfitting is when a model performs poorly on both training and unseen data due to lack of data.

• Overfitting refers to a model that generalizes well to new data but fails on training data.

• Overfitting is when a model performs well on training data but poorly on unseen data due to excessive complexity.

How do you handle missing data in a dataset?

• You can remove, impute, or use algorithms that handle missing data.

• Assume missing data is irrelevant and proceed without adjustments

• Ignore the missing data entirely

• Always fill missing data with zeros

What is the difference between training and testing data?

• Training data is for evaluation; testing data is for prediction.

• Training data is larger than testing data; testing data is smaller.

• Training data is for model training; testing data is for model evaluation.

• Training data is used for testing; testing data is for training.

What is cross-validation and why is it important?

• Cross-validation is a method to increase the size of the training dataset.

• Cross-validation is used to reduce the complexity of a model by removing features.

• Cross-validation is a technique to visualize data distributions.

• Cross-validation is important because it helps to assess how the results of a statistical analysis will generalize to an independent data set, thus preventing overfitting and ensuring the model's robustness.