I’m a just a beginner, I’m still learning about sparse matrices and how they work with other stuff.
Here is the problem I encountered, searched the web and couldn’t find a proper answer.

I OneHotEncoded the categorical labels with default params sparse_output=True,
When I tried to fit the RandomForestClassifier with the transformed_X and target y after train test splitting, it shows this error.

#seed
np.random.seed(42)

#one hot encoding imports
from sklearn.preprocessing import OneHotEncoder
from sklearn.compose import ColumnTransformer as ct

#Data splitting
X = f_data.drop('attended', axis = 1)
y = f_data['attended']

#select columns
cat_col = ['days_before','day_of_week','time','category']

#initialize encoder 
enc = OneHotEncoder()

#fitting for encoder using ct
transformer = ct([('enc',enc,cat_col)], remainder = 'passthrough')
transformed_X = transformer.fit_transform(X)
transformed_X

<1480x36 sparse matrix of type '<class 'numpy.float64'>' with 10360 stored elements in Compressed Sparse Row format>

#BaseLine model
np.random.seed(42)

#imports
from sklearn.model_selection import train_test_split as tts
from sklearn.ensemble import RandomForestClassifier

#splitting
X_train,Y_train,X_test,Y_test = tts(transformed_X,y, test_size = 0.2)

#model fitting
model = RandomForestClassifier()
model.fit(X_train,Y_train)

#model score
blsc = model.score(X_test,Y_test)
print(f'Baseline Model Score is : {blsc}')

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
Cell In[416], line 13
     11 #modelling
     12 model = RandomForestClassifier()
---> 13 model.fit(X_train,Y_train)
     15 #model score
     16 blsc = model.score(X_test,Y_test)

File G:Md JafferUDEMYMachine Learning Course ZTMProjectsHeartDesease_ClassificationenvLibsite-packagessklearnbase.py:1474, in _fit_context.<locals>.decorator.<locals>.wrapper(estimator, *args, **kwargs)
   1467     estimator._validate_params()
   1469 with config_context(
   1470     skip_parameter_validation=(
   1471         prefer_skip_nested_validation or global_skip_validation
   1472     )
   1473 ):
-> 1474     return fit_method(estimator, *args, **kwargs)

File G:Md JafferUDEMYMachine Learning Course ZTMProjectsHeartDesease_ClassificationenvLibsite-packagessklearnensemble_forest.py:361, in BaseForest.fit(self, X, y, sample_weight)
    359 # Validate or convert input data
    360 if issparse(y):
--> 361     raise ValueError("sparse multilabel-indicator for y is not supported.")
    363 X, y = self._validate_data(
    364     X,
    365     y,
   (...)
    369     force_all_finite=False,
    370 )
    371 # _compute_missing_values_in_feature_mask checks if X has missing values and
    372 # will raise an error if the underlying tree base estimator can't handle missing
    373 # values. Only the criterion is required to determine if the tree supports
    374 # missing values.

ValueError: sparse multilabel-indicator for y is not supported.

I tried to set the sparse_output=False and it gave the inconsistent numbers of samples error. Actual shape after label encoding is (1480 x 36)

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
Cell In[444], line 13
     11 #modelling
     12 model = RandomForestClassifier()
---> 13 model.fit(X_train,Y_train)
     15 #model score
     16 blsc = model.score(X_test,Y_test)

ValueError: Found input variables with inconsistent numbers of samples: [1184, 296]