ValueError: Unknown label type: array([0.11],...) when making extra trees model

Question

I was trying to use an extra trees classifier on this dataset, and for some reason at the

model.fit(trainx,trainy)

part, it throws me a

ValueError: Unknown label type: array([[ 0.11],
       [ 0.12],
       [ 0.64],
       [ 0.83],
       [ 0.33],
       [ 0.72],
       [ 0.49],

error. The array([0.11] is my trainy data. I've searched stack overflow and apparently its due to sklearn not recognizing the data type, but ive tried everything from

trainy = np.asarray(trainy,dtype=float)
trainy=trainy.astype(float)

and it doesnt work, even though type(trainy) shows that its numpy.ndarray. Can anyone point me in the right direction here?

Here's the code:

import pandas as pd
import numpy as np
from sklearn.preprocessing import LabelEncoder
from sklearn import metrics
from sklearn.ensemble import ExtraTreesClassifier
from sklearn import cross_validation


def preProcess():
    df= pd.read_csv('C:/Users/X/Desktop/Managerial_and_Decision_Economics_2013_Video_Games_Dataset.csv',encoding ='ISO-8859-1')
    #drop non EA
    df = df[df['EA'] ==1]
    #change categorical variables
    le = LabelEncoder()
    nonnumeric_columns=['Console','Title','Publisher','Genre']
    for feature in nonnumeric_columns:
        df[feature] = le.fit_transform(df[feature])
    #set dataset and target variables
    dataset =df.ix[:, df.columns != 'US Sales (millions)']
    target = df['US Sales (millions)']

    trainx, testx, trainy, testy = cross_validation.train_test_split(
        dataset, target, test_size=0.3, random_state=0)
    #attempt to fix error?
    trainx=np.array(trainx)
    trainy = np.asarray(trainy, dtype="float")
    return trainx,testx,trainy,testy

def classifier():
    model =  ExtraTreesClassifier(n_estimators=250,
                              random_state=0)
    model.fit(trainx,trainy)
    return model.score(testx,testy)


trainx,testx,trainy,testy=preProcess()

I'm using scikit-learn 0.17 on python 3.5

Answer 1

Your labels [[0.11], [ 0.12],.... . You should use ExtraTreesRegressor instead of ExtraTreesClassifier

From source code of ForestClassifier:

 y : array-like, shape = [n_samples] or [n_samples, n_outputs]
            The target values (class labels in classification, real numbers in
            regression).