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Is there a simple way to check if a model instance solves a classification or regression task in the scikit-learn library?
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If None , estimator is considered fitted if there exist an attribute that ends with a underscore and does not start with double underscore. The default error message is, “This %(name)s instance is not fitted yet. Call 'fit' with appropriate arguments before using this estimator.”
Scikit-learn is the most popular Python library for performing classification, regression, and clustering algorithms. It is an essential part of other Python data science libraries like matplotlib , NumPy (for graphs and visualization), and SciPy (for mathematics).
Three types of Machine Learning Models can be implemented using the Sklearn Regression Models: Reinforced Learning. Unsupervised Learning. Supervised Learning.
coef_ − array, shape(n_features,) or (n_targets, n_features) It is used to estimate the coefficients for the linear regression problem. It would be a 2D array of shape (n_targets, n_features) if multiple targets are passed during fit. Ex. (y 2D).
Use sklearn.base.is_classifier and/or is_regressor:
from sklearn.base import is_classifier, is_regressor
from sklearn.linear_model import LinearRegression
from sklearn.ensemble import RandomForestRegressor
from sklearn.ensemble import RandomForestClassifier
models = [LinearRegression(), RandomForestClassifier(), RandomForestRegressor()]
for m in models:
print(m.__class__.__name__, is_classifier(m), is_regressor(m))
Output:
# model_name is_classifier is_regressor
LinearRegression False True
RandomForestClassifier True False
RandomForestRegressor False True
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