In [1]: from sklearn.ensemble import RandomForestClassifier
In [2]: from sklearn import datasets
In [3]: from sklearn.externals import joblib
In [4]: iris = datasets.load_iris()
In [5]: X, y = iris.data, iris.target
In [6]: m = RandomForestClassifier(2).fit(X, y)
In [7]: m
Out[7]:
RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
max_depth=None, max_features='auto', max_leaf_nodes=None,
min_samples_leaf=1, min_samples_split=2,
min_weight_fraction_leaf=0.0, n_estimators=2, n_jobs=1,
oob_score=False, random_state=None, verbose=0,
warm_start=False)
In [8]: joblib.dump(m, "filename.cls")
如果您遵循scikitdocumentation关于模型持久性的内容
实际上,您可以使用
pickle.dump
代替joblib
,但是joblib
在压缩分类器中的numpy
数组方面做得非常好。在相关问题 更多 >
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