Machine Learning: Core Concepts

Which of the following is the primary goal of supervised learning?

Unsupervised learning algorithms require labeled data to train.

What type of machine learning is used when the data is unlabeled and the algorithm must discover hidden structures?

In machine learning, the process of optimizing model parameters to best fit the training data is known as ______.

Match the following machine learning tasks with their corresponding types:

Which evaluation metric is most suitable for assessing the performance of a classification model balancing precision and recall?

Overfitting occurs when a model performs well on the training data but poorly on unseen data.

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

The technique used to prevent overfitting by adding a penalty term to the loss function is called ______.

Match the following regularization techniques with their corresponding effects:

In the context of machine learning, what does the term 'bias' refer to?

A high-variance model is likely to underfit the training data.

What is the bias-variance tradeoff in machine learning?

The process of transforming features to a similar scale is called ______.

Match the following feature scaling techniques with their corresponding formulas:

Which of the following algorithms is commonly used for dimensionality reduction?

Dimensionality reduction always improves the performance of a machine learning model.

What is Feature Engineering?

The process of selecting a subset of relevant features from the original set is known as ______.

Match the following Feature Selection methods with their corresponding descriptions:

Which of the following is a common algorithm used for clustering data points?

In K-Means clustering, the number of clusters 'k' must always be determined automatically by the algorithm.

What statistical method can be used to estimate the optimal number of clusters 'k' in K-Means clustering?

The goal of K-Means clustering is to minimize the ______ within each cluster.

Match each of the following distances to their formulas in 2D space:

Which of the following techniques is commonly used to handle imbalanced datasets?

Undersampling the majority class always leads to a loss of important information.

Besides oversampling and undersampling, name another popular Technique utilized on Imbalanced Datasets.

The metric used to evaluate performance on imbalanced datasets that considers both precision and recall is the ______ score.

Match the following sampling techniques with their corresponding descriptions:

What is the purpose of hyperparameter tuning in machine learning?

Grid search is a hyperparameter tuning technique that explores all possible combinations of hyperparameter values.

What Hyperparameter tuning technique randomly samples combinations of hyperparameters from a defined range of values?

Bayesian optimization uses ______ to model the objective function and guide the search for the optimal hyperparameters.

Match the hyperparameter tuning techniques with their corresponding descriptions:

Which of the following machine learning techniques is most effective for time-series data?

In time series analysis, stationarity implies that the statistical properties of the series do not change over time.

Name a technique used to makes a time series stationary by removing trends and seasonality?

ARIMA models utilize ______, Integrated, and Moving Average components to forecast future values in a time series.

Match the components of the ARIMA model with their corresponding descriptions:

Insanely difficult: Which of the listed activation functions is known for addressing the vanishing gradient problem in deep neural networks?

Insanely difficult: Explain the concept of 'transfer learning' in machine learning and provide a specific example of its application.