Model Types

Binary Classification

Binary classification is a type of supervised learning where the goal is to predict one of two possible outcomes or classes. These models are used in a wide range of applications such as spam detection, fraud detection, and medical diagnosis.

Metrics : Accuracy, Recall, Precision, F- 1 Score, False Positive Rate(FPR) and False Negative Rate(FNR).

Multi-Class Classification

Multi-class classification is a type of supervised learning where the goal is to predict one of more than two possible outcomes or classes. These models are used in a wide range of applications such as image classification, speech recognition, and natural language processing.

Metrics : Accuracy, Recall, Precision, F- 1 Score, False Positive Rate(FPR) and False Negative Rate(FNR).

Prediction

Prediction models are a type of supervised machine learning model that are used to predict a continuous outcome variable (also called target or dependent variable) based on one or more input variables (also called features or independent variables). These models are used in a wide range of applications such as stock market forecasting, weather forecasting, and drug efficacy prediction.

Metrics : Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), R-Squared (Coefficient of Determination), Adjusted R-Squared and Mean Absolute Percentage Error (MAPE).

Detection

Detection models in machine learning are models that are used to identify and locate specific objects, events, or patterns in data, such as images, videos, or audio. These models are used in a wide range of applications such as object detection, face detection, anomaly detection, and fraud detection.

Metrics : Precision, Recall, F- 1 Score, Mean Average Precision (MAP), Intersection over Union (IoU), Receiver Operating Characteristic (ROC) Curve and AUC (Area Under the Curve), Detection Rate (DR), False Alarm Rate (FAR), Mean Time to Detection (MTTD) and Mean Time to False Alarm (MTTF).

Time Series

Time series models are statistical models used to analyze and forecast time series data, which are data points collected over time. Time series data is common in various fields, such as economics, finance, and weather forecasting.

Metrics : Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), R-Squared (Coefficient of Determination), Adjusted R-Squared and Mean Absolute Percentage Error (MAPE).