Understanding Classification Metrics

What is classification in machine learning?

• Classification is an unsupervised learning method for clustering data.

• Classification involves predicting continuous values from data.

• Classification is a supervised learning method used to assign labels to data based on training with labeled examples.

• Classification is a technique used only for image processing tasks.

Define accuracy in the context of classification.

• Accuracy measures the speed of classification algorithms.

• Accuracy is the difference between predicted and actual values.

• Accuracy is the number of true positives only.

• Accuracy is the proportion of true results (both true positives and true negatives) among the total number of cases examined.

What does a confusion matrix represent?

• A confusion matrix is used to visualize the training data of a model.

• A confusion matrix indicates the number of features in a dataset.

• A confusion matrix shows the distribution of data points in a dataset.

• A confusion matrix represents the performance of a classification model by comparing predicted and actual classifications.

How is precision calculated?

• Precision = True Negatives / (True Negatives + False Negatives)

• Precision = True Positives / (True Positives + False Positives)

• Precision = True Positives + False Positives

• Precision = True Positives / Total Samples

What is recall and why is it important?

• Recall measures the speed of a model's predictions.

• Recall is the total number of predictions made by a model.

• Recall is the percentage of irrelevant instances identified correctly.

• Recall is a measure of a model's ability to identify all relevant instances, calculated as true positives divided by the sum of true positives and false negatives.

Explain the concept of logistic regression.

• Logistic regression requires normally distributed data for accurate results.

• Logistic regression is used for predicting continuous outcomes.

• Logistic regression is a method for binary classification that models the probability of an outcome using the logistic function.

• Logistic regression is a clustering algorithm for grouping similar data points.

What are the main advantages of using KNN?

• KNN is a parametric algorithm that assumes a specific data distribution.

• The main advantages of using KNN are its simplicity, no training phase, versatility for classification and regression, adaptability to multi-class problems, and being non-parametric.

• KNN is only suitable for binary classification tasks.

• KNN requires extensive training data for accuracy.

Describe how a decision tree makes predictions.

• A decision tree uses a linear equation to make predictions.

• A decision tree makes predictions by randomly selecting a feature.

• A decision tree predicts by traversing from the root to a leaf node based on feature values.

• A decision tree predicts by averaging all feature values.

What is the difference between precision and recall?

• Precision measures the overall accuracy of the model; recall measures the speed of the model.

• Precision is the number of false positives divided by the total number of predictions; recall is the number of true negatives divided by the total number of actual positives.

• Precision and recall are both measures of the model's performance on negative cases.

• Precision is the ratio of true positives to the sum of true and false positives; recall is the ratio of true positives to the sum of true positives and false negatives.

How can you improve the accuracy of a classification model?

• Increase data quality and quantity, optimize features, and tune model parameters.

• Use a simpler model without tuning

• Reduce the number of features

• Ignore data preprocessing steps