Machine Learning Concepts

What is the main characteristic of Supervised Learning?

• Does not involve prediction

• Requires unsupervised data

• Does not need any data

• Requires labeled training data

Give an example of an Unsupervised Learning algorithm.

• Logistic Regression

• Linear Regression

• K-means clustering

• Decision Tree

Explain the concept of Neural Networks in machine learning.

• Neural Networks are algorithms that only work with numerical data in machine learning.

• Neural Networks are physical networks of wires and cables used in machine learning.

• Neural Networks are a type of software that can only be run on supercomputers in machine learning.

• Neural Networks are algorithms inspired by the human brain's structure, consisting of interconnected nodes that process data to make predictions.

How do Decision Trees work in the context of machine learning?

• Decision Trees split the data based on target values instead of feature values

• Decision Trees always select the first feature at each node

• Decision Trees split the data based on feature values to create a tree-like structure, selecting the best feature at each node to maximize information gain or minimize impurity.

• Decision Trees randomly assign feature values to create a tree-like structure

What is Regression Analysis used for in machine learning?

• Classifying images based on features

• Predicting continuous values based on the relationship between variables.

• Identifying outliers in a dataset

• Clustering data points into groups

Name a popular Clustering Algorithm used in machine learning.

• Hierarchical Clustering

• Linear Regression

• K-means

• DBSCAN

What is the difference between supervised and unsupervised learning?

• Supervised learning uses labeled data, while unsupervised learning does not.

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

• Supervised learning does not require a model, while unsupervised learning does.

• Supervised learning is used for classification tasks, while unsupervised learning is used for regression tasks.

How are weights adjusted in Neural Networks during training?

• By using optimization algorithms like Gradient Descent

• By asking the user

• By using a magic wand

• By flipping a coin

What is the purpose of using Decision Trees in machine learning?

• To calculate the mean and standard deviation of the data

• To perform linear regression on the data

• To make predictions by recursively splitting the data based on features to create a tree-like structure.

• To cluster data points into groups

Describe the process of K-means Clustering.

• K-means Clustering involves initializing centroids, assigning points to nearest centroid, updating centroids, and repeating until convergence.

• K-means Clustering assigns each point to the same centroid.

• K-means Clustering involves selecting random centroids initially.

• K-means Clustering updates centroids based on random values.