D491 Introduction to Analytics - Set 2 - Part 1

Correct Answer: K-means clustering

K-means clustering is an unsupervised learning algorithm used to group similar data points into clusters based on distance or similarity.

Correct Answer: One-hot encoding

One-hot encoding converts categorical variables into binary vectors, allowing them to be used in regression models.

Correct Answer: Discovery

The discovery phase focuses on understanding the business problem, identifying relevant data sources, and gathering the data needed for analysis.

Correct Answer: F1 Score

The F1 Score combines precision and recall into a single metric, providing a balanced evaluation of a binary classification model.

Correct Answer: To evaluate model performance by showing true positives, false positives, true negatives, and false negatives

A confusion matrix provides a detailed breakdown of the model's classification performance, showing the number of correct and incorrect predictions for each class.

Correct Answer: Association rules analysis

Association rules analysis is used to find relationships or patterns between different categorical variables, such as in market basket analysis.

Correct Answer: Mean absolute error (MAE)

Mean absolute error measures the average magnitude of errors in a regression model, providing an indication of how close predictions are to the actual values.

Correct Answer: Principal component analysis (PCA)

PCA reduces the dimensionality of a dataset by transforming the original features into new components that capture most of the variance.

Correct Answer: To visualize correlations between variables

Heat maps use color gradients to show the strength of correlations or relationships between variables in a dataset.

Correct Answer: Predicting binary outcomes

Logistic regression is used to predict the probability of a binary outcome, such as yes/no or true/false.

Correct Answer: Box plot

A box plot helps detect anomalies or outliers in a dataset by displaying the spread and quartiles of the data, with points outside the whiskers representing outliers.

Correct Answer: ARIMA

ARIMA (AutoRegressive Integrated Moving Average) is commonly used for time series forecasting, as it models trends and seasonality in time-dependent data.

Correct Answer: To avoid overfitting by testing the model on different data subsets

Cross-validation tests the model on multiple subsets of the data to ensure it generalizes well and is not overfitted to the training set.

Correct Answer: Histogram

A histogram shows the frequency distribution of a single continuous variable, providing insight into its spread and central tendency.

Correct Answer: Logistic regression

Logistic regression is widely used in supervised learning for classification tasks, where the goal is to predict the probability of a binary outcome.

Correct Answer: They are easy to interpret and visualize

Decision trees are easy to interpret and visualize, making them useful for understanding the decision-making process behind the model's predictions.

Correct Answer: Imputation

Imputation is the process of filling in missing data points with substituted values, such as the mean, median, or mode of the feature.

Correct Answer: Mean squared error (MSE)

Mean squared error measures the average squared difference between actual and predicted values, providing an indication of the model's accuracy.

Correct Answer: To visualize the relationship between two continuous variables

A scatter plot shows the correlation between two continuous variables by plotting individual data points on a Cartesian plane.

Correct Answer: Logistic regression

Logistic regression is commonly used for predicting binary outcomes, such as yes/no or true/false classifications.