machine learning final

K-nearest neighbors classifier

Classifies a new data point by the majority class among its K nearest neighbors

Support vector machine classifier

A supervised learning model that finds the hyperplane that best separates different classes in the feature space.

Decision tree classifier

A model that uses a tree-like graph of decisions and their possible consequences to classify data points based on features.

Random-forest classifier

An ensemble learning method that constructs multiple decision trees during training and outputs the mode of their predictions for classification tasks.

Bagging classifier

A technique that improves the stability and accuracy of machine learning algorithms by training multiple models on different subsets of the training data and averaging their predictions.

Neural network classifier

A computational model inspired by the human brain, consisting of interconnected nodes (neurons) that process data in layers to learn and classify patterns.

Adaboosting classifier

An ensemble learning technique that combines the predictions of several base estimators to improve accuracy by focusing on errors made by previous classifiers.

Accuracy

The measure of how often a classifier correctly predicts the target labels, typically expressed as the ratio of correctly predicted instances to the total instances.

Mcc score

A performance metric for binary classifiers that considers true and false positives and negatives to provide a balanced evaluation of model accuracy.

Sensitivity

measures how well a machine learning model can detect positive instances

Specificity

measures the proportion of True Negative which are correctly identified by the model

Precision

The proportion of true positive results in all positive predictions made by the classifier, indicating the accuracy of the positive class identification.

F1 score

The harmonic mean of precision and recall, providing a balance between the two metrics. It is particularly useful when evaluating models with imbalanced classes.