FICT_Day2_DataScience

What is the main goal of simple linear regression?

• To establish a linear relationship between variables for prediction.

• To determine the mode of the variables

• To identify outliers in the data

• To calculate the mean of the variables

Explain the concept of centroids in the k-means algorithm.

• Centroids are randomly assigned data points in the k-means algorithm

• Centroids in the k-means algorithm represent the center points of clusters and are recalculated iteratively based on the mean of data points in each cluster.

• Centroids are fixed points in the k-means algorithm that do not change during clustering

• Centroids in the k-means algorithm represent outliers in the dataset

How does a decision tree make predictions for classification?

• By recursively splitting the data based on features and asking questions at each node to navigate to leaf nodes for predictions.

• By predicting based on the first feature encountered in the dataset.

• By randomly selecting features for each split without considering data patterns.

• By always choosing the feature with the highest value regardless of data distribution.

What is the difference between supervised and unsupervised learning?

• Supervised learning uses labeled data, unsupervised learning uses labeled data.

• Supervised learning uses labeled data, unsupervised learning uses semi-supervised data.

• Supervised learning uses labeled data, unsupervised learning uses reinforcement learning.

• Supervised learning uses labeled data, unsupervised learning uses unlabeled data.

How is the optimal number of clusters determined in k-means clustering?

• Hierarchical Clustering

• Silhouette Score

• Elbow Method

• Davies-Bouldin Index