There are no NaNs in my dataset, I have checked thoroughly. Any reason why I keep getting this error when trying to fit my classifier? Some of the numbers in the data set are rather large and some decimal places go out 10 decimal points but I wouldn't thing that should cause an error. I included some of my pandas DataFrame info below as well as the error itself. Any ideas?
<class 'pandas.core.frame.DataFrame'> DatetimeIndex: 6244 entries, 1985-02-06 00:00:00 to 2009-11-05 00:00:00 Data columns (total 86 columns): dtypes: float64(86) clf = RandomForestClassifier(n_estimators=100,min_samples_split=4) clf.fit(train, train_target) --------------------------------------------------------------------------- ValueError Traceback (most recent call last) <ipython-input-150-fa4acb362bc6> in <module>() 1 clf = RandomForestClassifier(n_estimators=100,min_samples_split=4) ----> 2 clf.fit(train, train_target) 3 clf.score(test, test_target) C:\Anaconda\lib\site-packages\sklearn\ensemble\forest.pyc in fit(self, X, y, sample_weight) 255 # Convert data 256 X, = check_arrays(X, dtype=DTYPE, sparse_format="dense", --> 257 check_ccontiguous=True) 258 259 # Remap output C:\Anaconda\lib\site-packages\sklearn\utils\validation.pyc in check_arrays(*arrays, **options) 231 else: 232 array = np.asarray(array, dtype=dtype) --> 233 _assert_all_finite(array) 234 235 if copy and array is array_orig: C:\Anaconda\lib\site-packages\sklearn\utils\validation.pyc in _assert_all_finite(X) 25 if (X.dtype.char in np.typecodes['AllFloat'] and not np.isfinite(X.sum()) 26 and not np.isfinite(X).all()): ---> 27 raise ValueError("Array contains NaN or infinity.") 28 29 ValueError: Array contains NaN or infinity.