Introduction to Machine Learning

Which of the following best describes machine learning?

• A computer programming language

• A branch of artificial intelligence

• A type of hardware architecture A mathematical equation

• A mathematical equation

What is the main goal of supervised learning?

• To classify data into distinct categories

• To predict numerical values based on input data

• To discover hidden patterns or structures in data

• To optimize performance by trial and error

Which of the following is an example of unsupervised learning?

• Image Recognition

• Sentiment Analysis

• Clustering customer data

• Fraud Detection

What is the purpose of a training set in machine learning?

• To evaluate the performance of a model

• To fine-tune hyperparameters

• To test the generalization of a model

• • To train a model to make predictions

Which of the following algorithms is commonly used for decision tree learning?

• K-means clustering

• SVM

• Random Forest

• Naive Bayes

What does the term "overfitting" mean in machine learning?

• The model fails to capture the underlying patterns in the data

• The model performs well on the training data but poorly on new data

• The model is too simple to represent complex relationships in the data

• The model is too large to fit into the available memory

Which of the following evaluation metrics is commonly used for classification problems?

• MSE

• AUC

• R-Squared

• RMSE

what is the role of lambda in Lasso Regression

• Determining the learning rate for updating the coefficients

• represents the intercept term in lasso regression equation

• controls the amount of penalty applied to regression coefficients

• used to calculate the residual sum of squares

What is the purpose of feature scaling in machine learning?

• To eliminate missing values from the dataset

• To convert categorical variables into numerical representations

• To standardize or normalize features to a similar scale

• To select the most relevant features for the model

What is the purpose of cross-validation in machine learning?

• To split the dataset into training and testing sets

• To estimate the performance of a model on unseen data

• To find the best hyperparameters for a model

• To visualize the data in higher dimensions