In order to calculate score of different types of Algorithm we use following types of methods, few methods from SkLearn library are mentioned below.
| Scoring | Function | Comment |
|---|---|---|
| Classification | ||
| ‘accuracy’ | metrics.accuracy_score |
|
| ‘average_precision’ | metrics.average_precision_score |
|
| ‘f1’ | metrics.f1_score |
for binary targets |
| ‘f1_micro’ | metrics.f1_score |
micro-averaged |
| ‘f1_macro’ | metrics.f1_score |
macro-averaged |
| ‘f1_weighted’ | metrics.f1_score |
weighted average |
| ‘f1_samples’ | metrics.f1_score |
by multilabel sample |
| ‘neg_log_loss’ | metrics.log_loss |
requires predict_proba support |
| ‘precision’ etc. | metrics.precision_score |
suffixes apply as with ‘f1’ |
| ‘recall’ etc. | metrics.recall_score |
suffixes apply as with ‘f1’ |
| ‘roc_auc’ | metrics.roc_auc_score |
|
| Clustering | ||
| ‘adjusted_mutual_info_score’ | metrics.adjusted_mutual_info_score |
|
| ‘adjusted_rand_score’ | metrics.adjusted_rand_score |
|
| ‘completeness_score’ | metrics.completeness_score |
|
| ‘fowlkes_mallows_score’ | metrics.fowlkes_mallows_score |
|
| ‘homogeneity_score’ | metrics.homogeneity_score |
|
| ‘mutual_info_score’ | metrics.mutual_info_score |
|
| ‘normalized_mutual_info_score’ | metrics.normalized_mutual_info_score |
|
| ‘v_measure_score’ | metrics.v_measure_score |
|
| Regression | ||
| ‘explained_variance’ | metrics.explained_variance_score |
|
| ‘neg_mean_absolute_error’ | metrics.mean_absolute_error |
|
| ‘neg_mean_squared_error’ | metrics.mean_squared_error |
|
| ‘neg_mean_squared_log_error’ | metrics.mean_squared_log_error |
|
| ‘neg_median_absolute_error’ | metrics.median_absolute_error |
|
| ‘r2’ | metrics.r2_score |
Few Examples are as:
from sklearn.metrics import r2_score
import numpy as np
model.score(x_test, y_test)
r2_score(y_test,y_predict)
from sklearn.metrics import mean_squared_error
print(“Mean squared error: %.2f” % mean_squared_error(y_test,y_predict,multioutput=’raw_values’))
from sklearn.metrics import explained_variance_score
explained_variance_score(y_test,y_predict)
#note Best possible score is 1.0, lower values are worse.