Comparison#

Learn how to easily compare plots from different models.

  • Compare two models by plotting all values: plot1 + plot2

  • Compare the performance between two models: plot2 - plot1

Confusion matrix#

Added in sklearn-evaluation version 0.7.2

import matplotlib
from sklearn import datasets
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
from sklearn_evaluation import plot

matplotlib.rcParams["figure.figsize"] = (7, 7)
matplotlib.rcParams["font.size"] = 18

# get training and testing data
X, y = datasets.make_classification(
    1000, 20, n_informative=10, class_sep=0.80, n_classes=3, random_state=0
)

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)


# fit decision tree and random forest, return confusion matrices
tree_pred, forest_pred = [
    est.fit(X_train, y_train).predict(X_test)
    for est in [DecisionTreeClassifier(), RandomForestClassifier()]
]

Decision tree confusion matrix#

tree_cm = plot.ConfusionMatrix.from_raw_data(y_test, tree_pred, normalize=False)
../_images/compare_5_0.png

Random forest confusion matrix#

forest_cm = plot.ConfusionMatrix.from_raw_data(y_test, forest_pred, normalize=False)
../_images/compare_7_0.png

Compare confusion matrices#

compare = tree_cm + forest_cm
../_images/compare_9_0.png 
diff = forest_cm - tree_cm
../_images/compare_10_0.png

ROC#

logistic_score, forest_score = [
    est.fit(X_train, y_train).predict_proba(X_test)
    for est in [LogisticRegression(), RandomForestClassifier()]
]

Logistic regression ROC#

logistic_roc = plot.ROC.from_raw_data(y_test, logistic_score)
../_images/compare_14_0.png

Random forest ROC#

forest_roc = plot.ROC.from_raw_data(y_test, forest_score)
../_images/compare_16_0.png

Compare ROC#

compare = logistic_roc + forest_roc
../_images/compare_18_0.png

Classification report#

Added in sklearn-evaluation version 0.7.8

Decision tree classification report#

tree_cr = plot.ClassificationReport.from_raw_data(y_test, tree_pred)
../_images/compare_20_0.png

Random forest classification report#

forest_cr = plot.ClassificationReport.from_raw_data(y_test, forest_pred)
../_images/compare_22_0.png

Compare classification reports#

compare = tree_cr + forest_cr
../_images/compare_24_0.png 
diff = forest_cr - tree_cr
../_images/compare_25_0.png

Precision Recall Curve#

tree_score, forest_score = [
    est.fit(X_train, y_train).predict_proba(X_test)
    for est in [DecisionTreeClassifier(), RandomForestClassifier()]
]

Decision tree PR#

tree_pr = plot.PrecisionRecall.from_raw_data(
    y_test,
    tree_score,
    label=["Decision Tree Class 1", "Decision Tree Class 2", "Decision Tree Class 3"],
)
../_images/compare_29_0.png

Random forest PR#

forest_pr = plot.PrecisionRecall.from_raw_data(
    y_test,
    forest_score,
    label=["Random Forest Class 1", "Random Forest Class 2", "Random Forest Class 3"],
)
../_images/compare_31_0.png

Compare PR#

compare = tree_pr + forest_pr
../_images/compare_33_0.png