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"Found input variables with inconsistent numbers of samples" Have I done something wrong during the train_test_split?

I am trying to logistic Regression Model, and run some test but I keep getting this error. Not really sure what I have done differently to everyone else
from sklearn import preprocessing
X = df.iloc[:,:len(df.columns)-1]
y = df.iloc[:,len(df.columns)-1]ere
This is how I am separating my columns
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)
TTS
logReg = LogisticRegression(n_jobs=-1)
logReg.fit(X_train, y_train)
y_pred = logReg.predict(X_train)
mae = mean_absolute_error(y_test, y_pred)
print("MAE:" , mae)
ValueError Traceback (most recent call last)
Cell In [112], line 1
----> 1 mae = mean_absolute_error(y_test, y_pred)
2 print("MAE:" , mae)
File ~\AppData\Local\Programs\Python\Python310\lib\site-packages\sklearn\metrics\_regression.py:196, in mean_absolute_error(y_true, y_pred, sample_weight, multioutput)
141 def mean_absolute_error(
142 y_true, y_pred, *, sample_weight=None, multioutput="uniform_average"
143 ):
144 """Mean absolute error regression loss.
145
146 Read more in the :ref:`User Guide <mean_absolute_error>`.
(...)
194 0.85...
195 """
--> 196 y_type, y_true, y_pred, multioutput = _check_reg_targets(
197 y_true, y_pred, multioutput
198 )
199 check_consistent_length(y_true, y_pred, sample_weight)
200 output_errors = np.average(np.abs(y_pred - y_true), weights=sample_weight, axis=0)
File ~\AppData\Local\Programs\Python\Python310\lib\site-packages\sklearn\metrics\_regression.py:100, in _check_reg_targets(y_true, y_pred, multioutput, dtype)
66 def _check_reg_targets(y_true, y_pred, multioutput, dtype="numeric"):
67 """Check that y_true and y_pred belong to the same regression task.
68
69 Parameters
(...)
98 correct keyword.
99 """
--> 100 check_consistent_length(y_true, y_pred)
101 y_true = check_array(y_true, ensure_2d=False, dtype=dtype)
102 y_pred = check_array(y_pred, ensure_2d=False, dtype=dtype)
File ~\AppData\Local\Programs\Python\Python310\lib\site-packages\sklearn\utils\validation.py:387, in check_consistent_length(*arrays)
385 uniques = np.unique(lengths)
386 if len(uniques) > 1:
--> 387 raise ValueError(
388 "Found input variables with inconsistent numbers of samples: %r"
389 % [int(l) for l in lengths]
390 )
ValueError: Found input variables with inconsistent numbers of samples: [25404, 101612]
I thought it was the way I split the columns but that doesn't seem to be the issue
It works when the test size is 50/50 but no other test size works

You are comparing the predicted labels for the train set with the labels for the test set, which are of different sizes, hence the error.
Replace
y_pred = logReg.predict(X_train)
with
y_pred = logReg.predict(X_test)

"ValueError: Classification metrics can't handle a mix of multiclass and continuous-multioutput targets"

import numpy as np
from sklearn.metrics import multilabel_confusion_matrix
y_true = np.array(true_classes)
y_pred = np.array(predictions)
multilabel_confusion_matrix(y_true, y_pred, labels=CATEGORIES)
this is the error of the code i made
ValueError Traceback (most recent call last)
<ipython-input-23-9b9ba4a0d20a> in <module>()
----> 1 multilabel_confusion_matrix(y_true, y_pred, labels=CATEGORIES)
1 frames
/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py in _check_targets(y_true, y_pred)
93 raise ValueError(
94 "Classification metrics can't handle a mix of {0} and {1} targets".format(
---> 95 type_true, type_pred
96 )
97 )
ValueError: Classification metrics can't handle a mix of multiclass and continuous-multioutput targets
I want to create a confusion matrix with 3 classes. But I'm having this error, and I've searched from various sources but can't find a solution

How to generate confusion matrix?

I have a school projects with deep learning face recognition. I need reciprocal matrix to measure performance metrics like accuracy, precision. I tried the following codes for this. However, the y_test parameter gives an error. How can I solve this?
from sklearn.model_selection import train_test_split
x_train, x_test, y_train, y_test = train_test_split(img_array, img_labels,
shuffle=True, stratify=img_labels,
test_size=0.1, random_state=42)
print('Eğitim için eleman sayısı, yükseklik/genişlik ve kanal sayısı: ', x_train.shape)
print('Test için eleman sayısı, yükseklik/genişlik ve kanal sayısı: : ',x_test.shape)
print('Eğitimdeki örnek ve sınıf sayısı :', y_train.shape)
print('Testteki örnek ve sınıf sayısı : ',y_test.shape)
my code
cm = confusion_matrix(y_test, y_pred)
print(cm)
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
Input In [55], in <cell line: 1>()
----> 1 cm = confusion_matrix(y_test, y_pred)
2 print(cm)
File ~\anaconda3\lib\site-packages\sklearn\metrics\_classification.py:307, in confusion_matrix(y_true, y_pred, labels, sample_weight, normalize)
222 def confusion_matrix(
223 y_true, y_pred, *, labels=None, sample_weight=None, normalize=None
224 ):
225 """Compute confusion matrix to evaluate the accuracy of a classification.
226
227 By definition a confusion matrix :math:`C` is such that :math:`C_{i, j}`
(...)
305 (0, 2, 1, 1)
306 """
--> 307 y_type, y_true, y_pred = _check_targets(y_true, y_pred)
308 if y_type not in ("binary", "multiclass"):
309 raise ValueError("%s is not supported" % y_type)
File ~\anaconda3\lib\site-packages\sklearn\metrics\_classification.py:93, in _check_targets(y_true, y_pred)
90 y_type = {"multiclass"}
92 if len(y_type) > 1:
---> 93 raise ValueError(
94 "Classification metrics can't handle a mix of {0} and {1} targets".format(
95 type_true, type_pred
96 )
97 )
99 # We can't have more than one value on y_type => The set is no more needed
100 y_type = y_type.pop()
ValueError: Classification metrics can't handle a mix of multilabel-indicator and continuous-multioutput targets

I know I should not be providing this in the answer but am not able to add comments right now.
The classification report expects both the y_pred and y_test to be a 1-D array with class labels as integers.
The prediction of the TensorFlow model is mostly a 2D array with each entry being 1D array with class probability for a given row. So, you need to do some preprocessing on the y_pred.
I came across something similar a few weeks ago, I am gonna share a few lines of codes that may be helpful.
res = np.array(res)
res = res.flatten()
res = np.round(res)
Please note that the above code is for binary classification. For multilabel classification, you may use np.argmax.

ValueError: Found input variables with inconsistent numbers of samples: [1461, 366]

i have performed a linear regression in Python,using scikit-learn. the problem doesn't seem to concern the fit of variables but the mean squared error and r-squared. I know that the leght of "y" doesn't match the lenght of "pred".
from sklearn.metrics import r2_score, mean_squared_error
mse = mean_squared_error(y, pred)
r2 = r2_score(y,pred)#Best fit lineplt.scatter(x, y)
plt.plot(x,pred, color = 'Black', marker = 'o')
#Results
print("Mean Squared Error : ", mse)
print("R-Squared :" , r2)
print("Y-intercept :" , regressor.intercept_)
print("Slope :" , regressor.coef_)
the error is:
ValueError Traceback (most recent call last)
<ipython-input-126-729d70b35dd9> in <module>
1 from sklearn.metrics import r2_score, mean_squared_error
----> 2 mse = mean_squared_error(y, pred)
3 r2 = r2_score(y,pred)#Best fit lineplt.scatter(x, y)
4 plt.plot(x,pred, color = 'Black', marker = 'o')
5
~\anaconda3\lib\site-packages\sklearn\utils\validation.py in inner_f(*args, **kwargs)
61 extra_args = len(args) - len(all_args)
62 if extra_args <= 0:
---> 63 return f(*args, **kwargs)
64
65 # extra_args > 0
~\anaconda3\lib\site-packages\sklearn\metrics\_regression.py in mean_squared_error(y_true, y_pred, sample_weight, multioutput, squared)
333 0.825...
334 """
--> 335 y_type, y_true, y_pred, multioutput = _check_reg_targets(
336 y_true, y_pred, multioutput)
337 check_consistent_length(y_true, y_pred, sample_weight)
~\anaconda3\lib\site-packages\sklearn\metrics\_regression.py in _check_reg_targets(y_true, y_pred, multioutput, dtype)
86 the dtype argument passed to check_array.
87 """
---> 88 check_consistent_length(y_true, y_pred)
89 y_true = check_array(y_true, ensure_2d=False, dtype=dtype)
90 y_pred = check_array(y_pred, ensure_2d=False, dtype=dtype)
~\anaconda3\lib\site-packages\sklearn\utils\validation.py in check_consistent_length(*arrays)
260 uniques = np.unique(lengths)
261 if len(uniques) > 1:
--> 262 raise ValueError("Found input variables with inconsistent numbers of"
263 " samples: %r" % [int(l) for l in lengths])
264
ValueError: Found input variables with inconsistent numbers of samples: [1461, 366]

How can I get the probability of each predicted values in Python in multiclass?

I am currently working on a disease prediction machine learning model. I used Random Forest Classifier in my model, and now I am trying to get probabilities of predicted values, but the code gives me an error. In this program, I want to get the probabilities of "each" prediction specifically. For example, I entered the symptoms to predict the disease and the predicted disease is "Allergy". Then, I want my program to show the probability of the predicted disease "Allergy" as a percent, but the program gives errors like "classification metrics can't handle a mix of multiclass and unknown targets". I guess I need to use confusion matrix to show the probabilities, but it also gives the same error about the multiclass problem. More clearly, I just want to show the probability of each predicted value as "percent". For instance, the probability of Allergy disease is 90%, etc. How can I do that and how can I solve my problem?
Here the relevant codes:
p=pickle_model.predict([[22,8,50,9,20,47,50,38,0,0,0]])
actual=np.array((22,8,50,9,20,47,50,38,0,0,0))
pred=pickle_model.predict_proba([[p,0,0,0,0,0,0,0,0,0,0]])
In the code block below:
from sklearn.metrics import confusion_matrix
import sklearn.metrics as mt
from sklearn.metrics import accuracy_score
from sklearn.metrics import precision_score
print(accuracy_score(actual, p, normalize=True, sample_weight=None))
I get the error:
ValueError Traceback (most recent call last)
<ipython-input-69-e8980bf68410> in <module>
3 from sklearn.metrics import accuracy_score
4 from sklearn.metrics import precision_score
----> 5 print(accuracy_score(actual, p, normalize=True, sample_weight=None))
6 #precision, recall, fscore, support =
7 #score(y_test, p)
~\anaconda3\lib\site-packages\sklearn\utils\validation.py in inner_f(*args, **kwargs)
61 extra_args = len(args) - len(all_args)
62 if extra_args <= 0:
---> 63 return f(*args, **kwargs)
64
65 # extra_args > 0
~\anaconda3\lib\site-packages\sklearn\metrics\_classification.py in accuracy_score(y_true, y_pred, normalize, sample_weight)
200
201 # Compute accuracy for each possible representation
--> 202 y_type, y_true, y_pred = _check_targets(y_true, y_pred)
203 check_consistent_length(y_true, y_pred, sample_weight)
204 if y_type.startswith('multilabel'):
~\anaconda3\lib\site-packages\sklearn\metrics\_classification.py in _check_targets(y_true, y_pred)
81 y_pred : array or indicator matrix
82 """
---> 83 check_consistent_length(y_true, y_pred)
84 type_true = type_of_target(y_true)
85 type_pred = type_of_target(y_pred)
~\anaconda3\lib\site-packages\sklearn\utils\validation.py in check_consistent_length(*arrays)
260 uniques = np.unique(lengths)
261 if len(uniques) > 1:
--> 262 raise ValueError("Found input variables with inconsistent numbers of"
263 " samples: %r" % [int(l) for l in lengths])
264
ValueError: Found input variables with inconsistent numbers of samples: [11, 1]
Also, the other error that I get in this code block is:
ValueError Traceback (most recent call last)
<ipython-input-65-774dbd6b46f7> in <module>
8
9 # specificity
---> 10 tn, fp, fn, tp = mt.confusion_matrix(actual, predict).ravel()
11 specificity = tn / (tn+fp)
12 print(specificity)
~\anaconda3\lib\site-packages\sklearn\utils\validation.py in inner_f(*args, **kwargs)
61 extra_args = len(args) - len(all_args)
62 if extra_args <= 0:
---> 63 return f(*args, **kwargs)
64
65 # extra_args > 0
~\anaconda3\lib\site-packages\sklearn\metrics\_classification.py in confusion_matrix(y_true, y_pred, labels, sample_weight, normalize)
294
295 """
--> 296 y_type, y_true, y_pred = _check_targets(y_true, y_pred)
297 if y_type not in ("binary", "multiclass"):
298 raise ValueError("%s is not supported" % y_type)
~\anaconda3\lib\site-packages\sklearn\metrics\_classification.py in _check_targets(y_true, y_pred)
90
91 if len(y_type) > 1:
---> 92 raise ValueError("Classification metrics can't handle a mix of {0} "
93 "and {1} targets".format(type_true, type_pred))
94
ValueError: Classification metrics can't handle a mix of multiclass and unknown targets

The function you are looking is predict_proba().
The input of this function has to be exactly the inputs of your model trained. It must have the same inputs of the function predict().
If this part of your code worked to you (since you not reported any error):
p=pickle_model.predict([[22,8,50,9,20,47,50,38,0,0,0]])
This should work also:
p=pickle_model.predict_proba([[22,8,50,9,20,47,50,38,0,0,0]])
Note: Be aware of "[]", maybe you should have to delete someone.