CODEWITHSUNDEEP
machine-learningscikit-learnrandom-forestpredict
Sidhartha Mohapatra
Sidhartha Mohapatra

Reputation: 41

no error while fitting the model over train data but NotFittedError while predicting over test set

Not fitted error coming up when using .predict,during fit there is no error

tried to convert dataframe into arrays still same error

Input:

rfg(n_estimators=500,random_state=42).fit(X=data_withoutnull1.iloc[:,1:8],y=data_withoutnull1['LotFrontage'])
rfg(n_estimators=500,random_state=42).predict(datawithnull1.iloc[:,1:8])

Output:

Traceback (most recent call last):

  File "<ipython-input-477-10c6d72bcc12>", line 2, in <module>
    rfg(n_estimators=500,random_state=42).predict(datawithnull1.iloc[:,1:8])

  File "/home/sinikoibra/miniconda3/envs/pv36/lib/python3.6/site-packages/sklearn/ensemble/forest.py", line 691, in predict
    check_is_fitted(self, 'estimators_')

  File "/home/sinikoibra/miniconda3/envs/pv36/lib/python3.6/site-packages/sklearn/utils/validation.py", line 914, in check_is_fitted
    raise NotFittedError(msg % {'name': type(estimator).__name__})

NotFittedError: This RandomForestRegressor instance is not fitted yet. Call 'fit' with appropriate arguments before using this method.

Upvotes: 0

Views: 179

Answers (1)

011089dd
011089dd

Reputation: 1

Try like this :

# Define X and y
X=data_withoutnull1.iloc[:,1:8].values
y=data_withoutnull1['LotFrontage']

You can use train test split to split the data into training set and testing set then pass the testing set into predict.

#pass X_train to fit -- training the model, fit(X_train)
#pass X_test to predict -- can be used for prediction, predict(X_test )

or Fitting Random Forest Regression to the dataset

from sklearn.ensemble import RandomForestRegressor
rfg= RandomForestRegressor(n_estimators = 500, random_state = 42)
rfg.fit(X, y)

# Predicting a new result
y_pred = rfg.predict([[some value here]] or testing set or dataset to be predicted)

Upvotes: 0

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