Module smartcore::metrics

Functions for assessing prediction error.

Metric functions

One way to build machine learning models is to use a constructive feedback loop through model evaluation. In a feedback loop you build your model first, then you get feedback from metrics, improve it and repeat until your model achieve desirable performance. Evaluation metrics helps to explain the performance of a model and compare models based on an objective criterion.

Choosing the right metric is crucial while evaluating machine learning models. In smartcore you will find metrics for these classes of ML models:

• Classification metrics
• Regression metrics
• Clustering metrics

Example:

use smartcore::linalg::basic::matrix::DenseMatrix;
use smartcore::linear::logistic_regression::LogisticRegression;
use smartcore::metrics::*;

let x = DenseMatrix::from_2d_array(&[
            &[5.1, 3.5, 1.4, 0.2],
            &[4.9, 3.0, 1.4, 0.2],
            &[4.7, 3.2, 1.3, 0.2],
            &[4.6, 3.1, 1.5, 0.2],
            &[5.0, 3.6, 1.4, 0.2],
            &[5.4, 3.9, 1.7, 0.4],
            &[4.6, 3.4, 1.4, 0.3],
            &[5.0, 3.4, 1.5, 0.2],
            &[4.4, 2.9, 1.4, 0.2],
            &[4.9, 3.1, 1.5, 0.1],
            &[7.0, 3.2, 4.7, 1.4],
            &[6.4, 3.2, 4.5, 1.5],
            &[6.9, 3.1, 4.9, 1.5],
            &[5.5, 2.3, 4.0, 1.3],
            &[6.5, 2.8, 4.6, 1.5],
            &[5.7, 2.8, 4.5, 1.3],
            &[6.3, 3.3, 4.7, 1.6],
            &[4.9, 2.4, 3.3, 1.0],
            &[6.6, 2.9, 4.6, 1.3],
            &[5.2, 2.7, 3.9, 1.4],
  ]);
let y: Vec<i8> = vec![
            0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
  ];

let lr = LogisticRegression::fit(&x, &y, Default::default()).unwrap();

let y_hat = lr.predict(&x).unwrap();

let acc = ClassificationMetricsOrd::accuracy().get_score(&y, &y_hat);
// or
let acc = accuracy(&y, &y_hat);

Modules

• accuracy
  Accuracy score.
• auc
  Area Under the Receiver Operating Characteristic Curve (ROC AUC)
• cluster_hcv
  Compute the homogeneity, completeness and V-Measure scores.
• distance
  Multitude of distance metrics are defined here
• f1
  F1 score, also known as balanced F-score or F-measure.
• mean_absolute_error
  Mean absolute error regression loss.
• mean_squared_error
  Mean squared error regression loss.
• precision
  Computes the precision.
• r2
  Coefficient of determination (R2). Coefficient of Determination (R2)
• recall
  Computes the recall.

Structs

• ClassificationMetrics
  Use these metrics to compare classification models.
• ClassificationMetricsOrd
  Use these metrics to compare classification models for numbers that require Ord.
• ClusterMetrics
  Cluster metrics.
• RegressionMetrics
  Metrics for regression models.

Traits

• Metrics
  A trait to be implemented by all metrics

Functions

• accuracy
  Function that calculated accuracy score, see accuracy.
• completeness_score
  Completeness metric of a cluster labeling given a ground truth (range is between 0.0 and 1.0).
• f1
  Computes F1 score, see F1.
• homogeneity_score
  Homogeneity metric of a cluster labeling given a ground truth (range is between 0.0 and 1.0). A cluster result satisfies homogeneity if all of its clusters contain only data points which are members of a single class.
• mean_absolute_error
  Computes mean absolute error, see mean absolute error.
• mean_squared_error
  Computes mean squared error, see mean squared error.
• precision
  Calculated precision score, see precision.
• r2
  Computes R2 score, see R2.
• recall
  Calculated recall score, see recall
• roc_auc_score
  AUC score, see AUC.
• v_measure_score
  The harmonic mean between homogeneity and completeness.