Visualizations with Display Objects¶
In this example, we will construct display objects,
PrecisionRecallDisplay directly from their respective metrics. This
is an alternative to using their corresponding plot functions when
a model’s predictions are already computed or expensive to compute. Note that
this is advanced usage, and in general we recommend using their respective
Load Data and train model¶
For this example, we load a blood transfusion service center data set from
OpenML <https://www.openml.org/d/1464>. This is a binary classification
problem where the target is whether an individual donated blood. Then the
data is split into a train and test dataset and a logistic regression is
fitted wtih the train dataset.
from sklearn.datasets import fetch_openml from sklearn.preprocessing import StandardScaler from sklearn.pipeline import make_pipeline from sklearn.linear_model import LogisticRegression from sklearn.model_selection import train_test_split X, y = fetch_openml(data_id=1464, return_X_y=True) X_train, X_test, y_train, y_test = train_test_split(X, y, stratify=y) clf = make_pipeline(StandardScaler(), LogisticRegression(random_state=0)) clf.fit(X_train, y_train)
Pipeline(steps=[('standardscaler', StandardScaler()), ('logisticregression', LogisticRegression(random_state=0))])
With the fitted model, we compute the predictions of the model on the test dataset. These predictions are used to compute the confustion matrix which is plotted with the
The roc curve requires either the probabilities or the non-thresholded decision values from the estimator. Since the logistic regression provides a decision function, we will use it to plot the roc curve:
Similarly, the precision recall curve can be plotted using
y_scorefrom the prevision sections.
Combining the display objects into a single plot¶
The display objects store the computed values that were passed as arguments. This allows for the visualizations to be easliy combined using matplotlib’s API. In the following example, we place the displays next to each other in a row.
Total running time of the script: ( 0 minutes 1.933 seconds)