Data Science Quiz

What is machine learning?

• Machine learning is a process of training computers to perform tasks by providing them with labeled data.

• Machine learning is a field of study that focuses on developing algorithms and models that enable computers to learn and make predictions or decisions without being explicitly programmed.

• Machine learning is a type of software that can think and reason like a human.

• Machine learning is a technique used to repair and maintain machines.

What are the two main types of machine learning?

• linear regression and logistic regression

• reinforcement learning and deep learning

• supervised learning and unsupervised learning

• classification and regression

What is supervised learning?

• Supervised learning is a type of machine learning where an algorithm learns from labeled training data to make predictions or decisions.

• Supervised learning is a type of machine learning where an algorithm learns from unstructured training data to make predictions or decisions.

• Supervised learning is a type of machine learning where an algorithm learns from both labeled and unlabeled training data to make predictions or decisions.

• Supervised learning is a type of machine learning where an algorithm learns from unlabeled training data to make predictions or decisions.

What is unsupervised learning?

• Unsupervised learning is a type of machine learning where the model learns from labeled examples provided by a human.

• Unsupervised learning is a type of machine learning where the model predicts outcomes based on labeled examples.

• Unsupervised learning is a type of machine learning where the model learns patterns and relationships in data without any labeled examples or guidance from a human.

• Unsupervised learning is a type of machine learning where the model requires constant guidance from a human to learn patterns and relationships in data.

What is the difference between classification and regression in machine learning?

• Classification and regression are the same thing.

• Classification is for categorical variables, while regression is for continuous variables.

• Classification is used for numerical variables, while regression is used for textual variables.

• Classification is for continuous variables, while regression is for categorical variables.

What is the curse of dimensionality?

• The advantage of dimensionality

• The gift of dimensionality

• The blessing of dimensionality

• The curse of dimensionality

What is overfitting in machine learning?

• A model that is unable to learn from the training data and makes random predictions.

• A model that perfectly fits the training data and generalizes well to new data.

• A model that underfits the training data and fails to capture the underlying patterns.

• A model that memorizes the noise and outliers in the training data instead of learning the underlying patterns.

What is underfitting in machine learning?

• A model that is perfectly balanced and captures all patterns in the data.

• A model that is too simple and fails to capture the underlying patterns in the data.

• A model that is too complex and overfits the data.

• A model that is not trained on enough data and therefore cannot make accurate predictions.

What is feature selection in machine learning?

• Process of selecting relevant features from a larger set

• Process of selecting all features from a larger set

• Process of randomly selecting features from a larger set

• Process of selecting irrelevant features from a larger set

What is cross-validation in machine learning?

• Cross-validation is a method used to train a machine learning model.

• Cross-validation is a technique used to evaluate the performance of a machine learning model.

• Cross-validation is a process of selecting the best features for a machine learning model.

• Cross-validation is a technique used to preprocess the data for a machine learning model.