D491 Introduction to Analytics - Set 1 - Part 2

Correct Answer: Operationalize

The operationalize phase involves deploying the model in a production environment and monitoring its performance.

Correct Answer: To find interesting relationships between items in large datasets

Association rules are used in market basket analysis to discover relationships between items frequently purchased together.

Correct Answer: Line chart

Line charts are used to display data trends over a continuous time period.

Correct Answer: Clustering

Clustering can be used to identify data points that do not fit well with any group, which can be flagged as anomalies.

Correct Answer: Logistic regression

Logistic regression is widely used for classification tasks because it predicts the probability of binary outcomes.

Correct Answer: Imputation

Imputation involves filling in missing data with substituted values, such as the mean or median of the feature.

Correct Answer: Box plot

A box plot visualizes the spread and skewness of the data, highlighting the quartiles and outliers.

Correct Answer: Classification

Naive Bayes is commonly used for classification tasks, especially in text classification, because of its simplicity and efficiency.

Correct Answer: Histogram

A histogram is used to show the distribution of a single continuous variable by grouping data into bins.

Correct Answer: Random sampling

Random sampling is commonly used to split a dataset into training and test sets to evaluate model performance.

Correct Answer: Recall

Recall measures the proportion of actual positive instances correctly identified by the model.

Correct Answer: K-means clustering

K-means clustering is an unsupervised algorithm used to group data points into distinct clusters based on their similarity.

Correct Answer: To reduce the number of input variables

Feature selection reduces the number of input variables to simplify the model and avoid overfitting.

Correct Answer: Communicate results

The communicate results phase involves sharing the findings with stakeholders and making recommendations based on the analysis.

Correct Answer: Random forest reduces overfitting

Random forest reduces overfitting by averaging the results of multiple decision trees, making it more robust.

Correct Answer: Precision

Precision measures the proportion of positive predictions that are actually correct, making it a key metric for classification models.

Correct Answer: Principal component analysis (PCA)

PCA is a common dimensionality reduction technique that creates new variables (principal components) that preserve the variance in the dataset.

Correct Answer: To highlight correlations between variables

A heat map uses color to represent the strength of correlations or relationships between variables in a dataset.

Correct Answer: Naive Bayes

Naive Bayes is a probabilistic classifier that calculates the probability of an event occurring based on prior knowledge of conditions.

Correct Answer: Cross-validation

Cross-validation involves splitting the dataset into multiple subsets and training the model on each subset to reduce overfitting.