RandomForestClassfier.fit(): ValueError: could not convert string to float

Given is a simple CSV file:


Obviously the real dataset is far more complex than this, but this one reproduces the error. I'm attempting to build a random forest classifier for it, like so:

    cols = ['A','B','C']
    col_types = {'A': str, 'B': str, 'C': int}
    test = pd.read_csv('test.csv', dtype=col_types)

    train_y = test['C'] == 1
    train_x = test[cols]

    clf_rf = RandomForestClassifier(n_estimators=50)
    clf_rf.fit(train_x, train_y)

But I just get this traceback when invoking fit():

    ValueError: could not convert string to float: 'Bueno'

scikit-learn version is 0.16.1.

You have to do some encoding before using fit. As it was told fit() does not accept Strings but you solve this.

There are several classes that can be used :

  • LabelEncoder : turn your string into incremental value
  • OneHotEncoder : use One-of-K algorithm to transform your String into integer

Personally I have post almost the same question on StackOverflow some time ago. I wanted to have a scalable solution but didn't get any answer. I selected OneHotEncoder that binarize all the strings. It is quite effective but if you have a lot different strings the matrix will grow very quickly and memory will be required.

From: stackoverflow.com/q/30384995