Machine Learning Concepts

These flashcards cover key vocabulary and concepts related to machine learning, providing definitions and explanations to aid in understanding.

Machine Learning

A field of computer science that allows machines to learn from data and improve their performance on tasks without being explicitly programmed.

Supervised Learning

A type of machine learning where the model is trained on labeled data, which means each training example is paired with an output label.

Unsupervised Learning

A type of machine learning where the model is trained on data without labels, aiming to identify patterns or clusters within the data.

Reinforcement Learning

A type of machine learning where an agent learns to make decisions by taking actions in an environment to maximize cumulative reward.

K-nearest Neighbors (KNN)

A simple, instance-based learning algorithm used for classification and regression, where the outcome is based on the closest training examples in the feature space.

Precision

A performance metric that measures the accuracy of positive predictions; defined as the ratio of true positives to the total number of predicted positives.

Recall (Sensitivity)

A performance metric that measures the ability of a model to identify positive instances; defined as the ratio of true positives to the total number of actual positives.

F1 Score

The harmonic mean of precision and recall, providing a single score that balances both metrics.

Confusion Matrix

A table used to evaluate the performance of a classification model by comparing the predicted classifications against the actual classifications.

Gradient Descent

An optimization algorithm used to minimize a function by iteratively moving towards the steepest descent, used in training machine learning models.

Overfitting

A modeling error that occurs when a model learns to perform very well on training data but fails to generalize to unseen data.

Underfitting

A modeling error that occurs when a model is too simple to capture the underlying patterns in the data, resulting in poor performance on both training and unseen data.

Support Vector Machines (SVM)

A supervised learning model that finds the hyperplane that best separates data points of different classes.

Logistic Regression

A statistical method used for binary classification that models the probability of a discrete outcome based on one or more predictor variables.

Activation Function

A function applied to the output of a neuron in a neural network, determining whether it should be activated or not.

Hyperparameter

A parameter whose value is set before the learning process begins, influencing the behavior and performance of the learning algorithm.