Category : scikit-learn

I’m using the StandardScalar() and lin_reg.coef_ function in the following context: for i in range(100): x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=0.3,random_state=i) scaler = StandardScaler().fit(x_train) x_train = scaler.transform(x_train) x_test = scaler.transform(x_test) lin_reg = LinearRegression().fit(x_train, y_train) if i == 0: print(lin_reg.coef_) if i == 1: print(lin_reg.coef_) This leads to the following output: Code Output So, as have been expected, the ..

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I want to do a FeatureSelection with the mlxtend ExhaustiveFeatureSelector. My current Pipeline is simply: regr = make_pipeline(StandardScaler(), SVR()) I have also categorical attributes, which i have manually encoded with the OneHotEncoder() before setting up the pipeline. df = onehot(df,"Categorical_Attribute") X = df.drop(‘Target_Variable’, axis=1) y = df[‘Target_Variable’] Now i want start the Feature Selection but ..

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I’m trying to utilize make_pipeline() from scikit-learn along with GridSearchCV(). The Pipeline is simple and only includes two steps, a StandardScaler() and an MLPRegressor(). The GridSearchCV()is also pretty simple with the slight wrinkle that I’m using TimeSeriesSplit() for cross-validation. The error I’m getting is as follows: ValueError: Invalid parameter MLPRegressor for estimator Pipeline(steps=[(‘standardscaler’, StandardScaler()),(‘mlpregressor’, MLPRegressor())]). ..

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I’m trying to utilize make_pipeline() from scikit-learn along with GridSearchCV(). The Pipeline is simple and only includes two steps, a StandardScaler() and an MLPRegressor(). The GridSearchCV()is also pretty simple with the slight wrinkle that I’m using TimeSeriesSplit() for cross-validation. The error I’m getting is as follows: ValueError: Invalid parameter MLPRegressor for estimator Pipeline(steps=[(‘standardscaler’, StandardScaler()),(‘mlpregressor’, MLPRegressor())]). ..

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enter image description here Please, help. I’m trying to encode the ‘Embarked’ column by OneHotEncoder from Scikit-Learn. I’ve tryed to run this code and got that error. What’s wrong? from sklearn.preprocessing import OneHotEncoder onehotencoder = OneHotEncoder(categories = 11) df = onehotencoder.fit_transform(df).toarray() Source: Python..

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I try to implement CRF model to my NER application. I followed the tutorial in the official website from sklearn-crfsuite and generated the features using word2features function https://sklearn-crfsuite.readthedocs.io/en/latest/tutorial.html . However, when I tried to fit the model with X, y using crf.fit(X_train, y_train), I got the TypeError: C:ProgramDataAnaconda3libsite-packagessklearn_crfsuiteestimator.py in fit(self, X, y, X_dev, y_dev) 312 ..

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