Data Science

Correct Answer: Supervised learning involves labeled data, while unsupervised learning involves unlabeled data

Supervised learning involves having labeled data, which means that the target variable is known. Supervised learning is used for classification and regression problems. In contrast, unsupervised learning involves finding hidden patterns in the data with no labels to guide the algorithm.

Correct Answer: Regularization is a technique used to prevent overfitting in machine learning models by adding a penalty term to the objective function. It is important in machine learning to avoid overfitting which arises when a model becomes too complex and fits the noise in the data as well as the signal.

Regularization is a technique that adds a penalty term to the objective function to prevent overfitting, which is when the model fits the training data too closely and fails to generalize to new data. The penalty term is a function of the model parameters that penalizes large parameter values, encouraging the model to choose simpler solutions. L1 and L2 regularization are the most common penalty terms used in machine learning. L1 regularization encourages the model to produce sparse solutions (with many zero coefficients), while L2 regularization encourages the model to distribute its coefficient values more evenly. Regularization is important in machine learning to avoid overfitting and improve the generalization performance of the model. Therefore, it is commonly used in models such as linear regression, logistic regression, and support vector machines.

Correct Answer: To ensure accurate and reliable analysis

Data cleaning is essential to ensure that the data is accurate, complete, and reliable for analysis. Removing outliers, handling missing values, correcting data inconsistencies, and dealing with duplicates are just a start to data cleaning.