Machine Learning Basics

What is the main characteristic of Supervised Learning?

• Utilizes reinforcement learning

• Does not need any data

• Requires unsupervised training data

• Requires labeled training data

Give an example of an Unsupervised Learning algorithm.

• K-means clustering

• Linear Regression

• Support Vector Machine

• Decision Tree

What is the basic building block of Neural Networks?

• cell

• synapse

• axon

• neuron

How does a Decision Tree make predictions?

• By recursively splitting the data based on features and making decisions at each node until a prediction is reached.

• By always choosing the feature with the highest value.

• By randomly selecting features without considering data distribution.

• By predicting based on the first data point encountered.

Explain the concept of Support Vector Machines.

• Support Vector Machines only work with linearly separable data

• Support Vector Machines cannot handle high-dimensional data

• Support Vector Machines is an unsupervised learning algorithm

• Support Vector Machines (SVM) is a supervised machine learning algorithm used for classification and regression tasks. It finds the hyperplane with the maximum margin to separate different classes in the feature space, and can handle non-linear data using kernel functions.

What are some common Model Evaluation Techniques?

• Specificity

• Sensitivity

• Confusion Matrix, Accuracy, Precision, Recall, F1 Score, ROC Curve, AUC-ROC

• Mean Squared Error

• Kappa Statistic

What is the purpose of cross-validation in model evaluation?

• To increase the number of features

• To assess model generalization and performance while detecting overfitting.

• To reduce the training time

• To make the model more complex

How does overfitting affect the performance of a machine learning model?

• Overfitting affects the performance of a machine learning model by reducing its ability to generalize to new, unseen data, resulting in poor accuracy and predictive power.

• Overfitting improves the generalization ability of the model

• Overfitting has no impact on the model's accuracy

• Overfitting leads to better performance on unseen data

What is the difference between precision and recall?

• Precision measures the ability to find all positive instances, while recall measures the accuracy of positive predictions.

• Precision focuses on the accuracy of positive predictions, while recall focuses on finding all positive instances.

• Precision is more important than recall in classification tasks, while recall is more important in regression tasks.

• Precision is the ratio of true positive predictions to all positive predictions, while recall is the ratio of true positive predictions to all actual positive instances.

When would you use a regression model instead of a classification model?

• When you want to predict a binary outcome variable.

• When you want to predict a continuous outcome variable.

• When you want to use decision trees.

• When you want to classify data into groups.