• Core Algorithms
• Fitness Functions
• Math & Boolean Functions

• Data Visualization & Analysis
• Data Loading & Manipulation
• Analyses & Computations
• Panels & Windows

• Regression
• Classification
• Logistic Regression
• Time Series Prediction
• Logic Synthesis

• Model Evolution & Selection
• Model Visualization & Analysis
• Ensemble Methods

• Model Deployment
• Ensemble Deployment
• Predictions & Forecasting
• Programming Languages

• GEP paper (pdf)
• GEP Book (2nd Edition)

Mean Squared Error

where P_(ij) is the value predicted by the individual model i for record j (out of n records); and T_j is the target value for record j.

For a perfect fit, P_(ij) = T_j and E_i = 0. So, the E_i index ranges from 0 to infinity, with 0 corresponding to the ideal.

Measures of Fit for Regression

Measures of Fit for Classification

Measures of Fit for Logistic Regression

Measures of Fit for Time Series Prediction

Measures of Fit for Logic Synthesis

Classification Accuracy

Classification Error

Sensitivity

Specificity

Positive Predictive Value

Negative Predictive Value

Recall

Precision

F1 Measure

Jaccard Similarity

Matthews Correlation Coefficient

Area Under the ROC Curve

Pearson Correlation Coefficient

R-square

Root Mean Squared Error

Relative Absolute Error

Mean Absolute Error

Relative Squared Error

Root Relative Squared Error

Up/Down Accuracy

Up/Down Error

Getting Started with Regression

Getting Started with Classification

Getting Started with Logistic Regression

Getting Started with Time Series Prediction

Getting Started with Logic Synthesis

What's New in GeneXproTools 5.0?