RandomForestClassfier.fit(): ValueError: could not convert string to float
Full error message
Given is a simple CSV file:
A,B,C
Hello,Hi,0
Hola,Bueno,1
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.Solutionsource: stackoverflow \u2197
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 Stack Overflow 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 of different strings the matrix will grow very quickly and memory will be required.
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