8/26/2023 0 Comments Python random forest classifierThe common option here is one-hot encoding or converting into integers. We need to convert our categorical columns into numerical values. ![]() Hmmm …, this is strange, the Random Forest is an ensemble of decision trees, and decision trees should work with categorical values … Quick googling, and you got confirmation that scikit-learn Random Forest doesn’t work with categorical values and that somebody is working on this in sklearn ( stackoverflow link). We have ValueError, because there is ‘Private’ value in the workclass column. py in asarray ( a, dtype, order ) 536 -> 538 return array ( a, dtype, copy = False, order = order ) 539 540 ValueError : could not convert string to float : 'Private' 6 / site - packages / numpy / core / numeric. asarray ( array, dtype = dtype, order = order ) 528 except ComplexWarning : 529 raise ValueError ( "Complex data not supported \n " ~/ sandbox / rf / automl - rf / venv / lib / python3. simplefilter ( 'error', ComplexWarning ) -> 527 array = np. py in check_array ( array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator ) 525 try : 526 warnings. 6 / site - packages / sklearn / utils / validation. > 250 X = check_array ( X, accept_sparse = "csc", dtype = DTYPE ) 251 y = check_array ( y, accept_sparse = 'csc', ensure_2d = False, dtype = None ) 252 if sample_weight is not None : ~/ sandbox / rf / automl - rf / venv / lib / python3. py in fit ( self, X, y, sample_weight ) 248 249 # Validate or convert input data 6 / site - packages / sklearn / ensemble / forest. fit ( X_train, y_train ) ~/ sandbox / rf / automl - rf / venv / lib / python3. In this case, you can create your own classes and manually change variableĮg : regroup Turtle, Fish, Dolphin, Shark in a same class called Sea Animals and then appy a OneHotEncoding.- ValueError Traceback ( most recent call last ) 1 rf = RandomForestClassifier ( n_estimators = 1000 ) -> 2 rf = rf. Then it will create 50 binary variables, which can cause complexity issues. If you have a categorical variable encountering 50 kind of classes Then you can give this to your model, and he will never interpret that Dog is closer from Cat than from Turtle.īut there are also cons to OneHotEncoding. Then if you have Animal = "Dog", encoding will make it Dog = 1, Cat = 0, Turtle = 0. In my example, it will create 3 binary variables : Dog, Cat and Turtle. It will create as much variable as classes you encounter. Let's take back the previous example of Animal =. OneHot Encoding (also done by pd.get_dummies) is the best solution when you have no natural order between your variables.You have a natural order on your variables ![]() Child is closer than Teenager than it is from Young Adult. Label encoding is actually excellent when you have ordinal variable.įor example, if you have a value Age = , If you parse it to your machine learning model, it will interpret Dog is closer than Cat, and farther than Turtle (because distance between 1 and 2 is lower than distance between 1 and 3). If you use Label Encoder on it, Animal will be. Let's take the example of a variable Animal =. In this case, the 1st class found will be coded as 1, the 2nd as 2. Label Encoding will basically switch your String variables to int.Well, there are important differences between how OneHot Encoding and Label Encoding work : Train_data = le.fit_transform(train_data) The following code works for me and I hope this will help you. In the Pandas dataframe, I have to encode all the data which are categorized to dtype:object. ![]() Then you are able to transfer by OneHotEncoder as you wish. You may use LabelEncoder to transfer from str to continuous numerical values. "ValueError: could not convert string to float" may happen during transform. However OneHotEncoder does not support to fit_transform() of string. In scikit-learn, OneHotEncoder and LabelEncoder are available in inpreprocessing module. ![]() You have to transfer those str "A","B","C" to matrix by encoder like the following: A = īecause the str does not have numerical meaning for the classifier. If you have a feature column named 'grade' which has 3 different grades: You may not pass str to fit this kind of classifier.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |