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::naive::dense_matrix::*;
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<f64> = 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 = ClassificationMetrics::accuracy().get_score(&y, &y_hat);
// or
let acc = accuracy(&y, &y_hat);

Modules

accuracy	Accuracy score.
auc	Computes Area Under the Receiver Operating Characteristic Curve (ROC AUC) from prediction scores.
cluster_hcv	Compute the homogeneity, completeness and V-Measure scores.
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.
ClusterMetrics	Cluster metrics.
RegressionMetrics	Metrics for regression models.

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.