Machine Learning Concepts

Flashcards covering key terms and concepts related to machine learning and logistic regression.

Logistic Regression

A statistical method for predicting binary classes by using a logistic function.

Linear Classification

A classification approach that models the relationship between input features and classes using linear boundary.

Decision Boundary

A line or surface that separates different classes in a classification problem.

Linearly Separable

A condition where classes can be separated by a linear decision boundary.

Threshold Function

A function that determines the output of a model based on whether a linear function exceeds a certain threshold.

Minimizing Loss

The process of adjusting model parameters to reduce the difference between predicted and actual outcomes.

Perceptron Learning Rule

An algorithm for updating weights in binary classification problems based on prediction errors.

Logistic Function

A sigmoid function that produces outputs between 0 and 1, representing probabilities.

Probabilistic Interpretation

Understanding model outputs as probabilities indicating the likelihood of a class assignment.

One-vs-the-Rest Classifier

A method where multiple binary classifiers distinguish one class against all others.

Confusion Matrix

A table used to evaluate the performance of a classification model by showing true vs predicted classifications.

Sensitivity

The ratio of true positives to the sum of true positives and false negatives, indicating the ability to detect positive instances.

Specificity

The ratio of true negatives to the sum of true negatives and false positives, indicating the ability to identify negative instances.

Precision

The ratio of true positives to the sum of true positives and false positives, indicating the accuracy of positive predictions.