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Decision tree could not convert string to float how to#

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Could not load dynamic library 'cudart64_101.dll' on tensorflow CPU-only installation.Real time face detection OpenCV, Python.Get Public URL for File - Google Cloud Storage - App Engine (Python).is it possible to add colors to python output?.Comparing a variable with a string python not working when redirecting from bash script.Why my regexp for hyphenated words doesn't work?.Is there a way to view two blocks of code from the same file simultaneously in Sublime Text?.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. 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.