I need to fit RandomForestRegressor from sklearn.ensemble.

forest = ensemble.RandomForestRegressor(**RF_tuned_parameters)
model = forest.fit(train_fold, train_y)
yhat = model.predict(test_fold)

This code always worked until I made some preprocessing of data (train_y). The error message says:

DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().

model = forest.fit(train_fold, train_y)

Previously train_y was a Series, now it’s numpy array (it is a column-vector). If I apply train_y.ravel(), then it becomes a row vector and no error message appears, through the prediction step takes very long time (actually it never finishes…).

In the docs of RandomForestRegressor I found that train_y should be defined as y : array-like, shape = [n_samples] or [n_samples, n_outputs] Any idea how to solve this issue?

Change this line:

model = forest.fit(train_fold, train_y)

to:

model = forest.fit(train_fold, train_y.values.ravel())

Edit:

.values will give the values in an array. (shape: (n,1)

.ravel will convert that array shape to (n, )

I also encountered this situation when I was trying to train a KNN classifier. but it seems that the warning was gone after I changed:
knn.fit(X_train,y_train)
to
knn.fit(X_train, np.ravel(y_train,order='C'))

Ahead of this line I used import numpy as np.

I had the same problem. The problem was that the labels were in a column format while it expected it in a row. use np.ravel()

knn.score(training_set, np.ravel(training_labels))

Hope this solves it.

use below code:

model = forest.fit(train_fold, train_y.ravel())

if you are still getting slap by error as identical as below ?

Unknown label type: %r" % y

use this code:

y = train_y.ravel()
train_y = np.array(y).astype(int)
model = forest.fit(train_fold, train_y)

Another way of doing this is to use ravel

model = forest.fit(train_fold, train_y.values.reshape(-1,))

With neuraxle, you can easily solve this :

p = Pipeline([
   # expected outputs shape: (n, 1)
   OutputTransformerWrapper(NumpyRavel()), 
   # expected outputs shape: (n, )
   RandomForestRegressor(**RF_tuned_parameters)
])

p, outputs = p.fit_transform(data_inputs, expected_outputs)

Neuraxle is a sklearn-like framework for hyperparameter tuning and AutoML in deep learning projects !

format_train_y=[]
for n in train_y:
    format_train_y.append(n[0])

Y = y.values[:,0]

Y – formated_train_y

y – train_y