get_confusion_matrix
Build a confusion matrix , such that is equal to the number of observations known to be in group but predicted to be in group .
Method call format
get_confusion_matrix(model, data, thread_count)
Parameters
model
Description
The trained model.
Possible types
catboost.CatBoost
Default value
Required parameter
data
Description
A set of samples to build the confusion matrix with.
Possible types
catboost.Pool
Default value
Required parameter
thread_count
Description
The number of threads to use.
Possible types
int
Default value
-1 (the number of threads is set to the number of CPU cores)
Type of return value
confusion matrix : array, shape = [n_classes, n_classes]
Examples
Multiclassification
from catboost import Pool, CatBoostClassifier
from catboost.utils import get_confusion_matrix
train_data = [[1, 1924, 44],
[1, 1932, 37],
[0, 1980, 37],
[1, 2012, 204]]
train_label = ["France", "USA", "USA", "UK"]
train_dataset = Pool(data=train_data,
label=train_label)
model = CatBoostClassifier(loss_function='MultiClass',
iterations=100,
verbose=False)
model.fit(train_dataset)
cm = get_confusion_matrix(model, Pool(train_data, train_label))
print(cm)
Output:
[[1. 0. 0.]
[0. 1. 0.]
[0. 0. 2.]]
Binary classification
from catboost import Pool, CatBoostClassifier
from catboost.utils import get_confusion_matrix
train_data = [[1, 1924, 44],
[1, 1932, 37],
[0, 1980, 37],
[1, 2012, 204]]
train_label = [0, 1, 1, 0]
train_dataset = Pool(data=train_data,
label=train_label)
model = CatBoostClassifier(loss_function='Logloss',
iterations=100,
verbose=False)
model.fit(train_dataset)
cm = get_confusion_matrix(model, Pool(train_data, train_label))
print(cm)
Output:
[[2. 0.]
[0. 2.]]