I have data with multiple labels, for example
My X set is fromt second to third column, and I want to classify either first column or the last column, so I made my Y the last column.
The goal is so that if I would classify Vios it would return me Car or 0 in other words it can find its way to the first row.
Classification use case:
classify("poodle") #just pretend this is a working function
How I did it in attempt to train my model:
from sklearn.feature_extraction.text import TfidfVectorizer X_train, X_test, y_train, y_test = train_test_split(X, y, random_state = 72) count_vect = CountVectorizer() X_train_counts = count_vect.fit_transform(X_train) tfidf_transformer = TfidfTransformer() X_train_tfidf = tfidf_transformer.fit_transform(X_train_counts) clf3 = RandomForestClassifier().fit(X_train_tfidf, y_train)
I'm using a guide from somewhere on the net that works a bit the same with it, but at the end im getting returned:
ValueError: Found input variables with inconsistent numbers of samples: [5, 4156]
I knew immediately i was doing it wrong. How do I train a model so that it achieves my goal? Any relevant guides or techniques I should be following intead of this? I dont even know the correct way to use vectors on this case.