Data Science Quiz

Extracting valuable information from the target variable

Creating new features or modifying existing ones to improve model performance

Selecting the most important features for model training

Normalizing feature values to have zero mean and unit variance

Modifying features to have comparable scales

Creating new features from existing ones

Removing irrelevant features from the dataset

Encoding categorical variables

Representing words as sparse binary vectors

Encoding words into numerical vectors with continuous values

Tokenizing sentences into individual words

Reducing the dimensionality of word representations

The probability of making a Type II error

The probability of observing the data given that the null hypothesis is true

The significance level for the test

The probability of rejecting the null hypothesis

The trade-off between the number of features and model complexity

Balancing precision and recall in classification problems

The trade-off between model flexibility and stability

Minimizing both training and testing errors

Estimating parameters based on prior knowledge and observed data

Fitting models to the training data using maximum likelihood estimation

Combining predictions from multiple models using Bayesian averaging

Evaluating models using cross-validation

The size of the steps taken during each iteration

The regularization strength applied to the mod

The number of iterations in the optimization process

The speed at which the algorithm converges

A measure of impurity or disorder in a set of data

A measure of information gain in feature selection

A criterion used to split nodes in a decision tree

A method for pruning decision trees

Improving model interpretability

Reducing the learning rate during training

Introducing non-linearity to the model

Preventing overfitting by randomly dropping neurons during training

The ratio of true positive predictions to the total positive predictions

The ratio of true positive predictions to the sum of true positives and false negatives

The ratio of true positive predictions to the sum of true positives and false positives

The ratio of true positive predictions to the total predictions made by the model