svclassifier.fit(X_train, y_train)
принимает массив в качестве входных данных, но вы передаете панду DataFrame. Попробуйте использовать sklearn.preprocessing.LabelEncoder
istead из pd.get_dummies
.
Редактировать: Пример с LabelEncoder
и OneHotEncoder
:
# create a simple dataset of people
data = {'Name': ["John", "Anna", "Peter", "Linda","John","John","John"],
'Location' : ["Paris","Paris","Paris","Paris", "New York", "Berlin", "London"],
'Age' : [24, 23, 21, 24,36,34,36]
}
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
from sklearn.svm import SVC
import numpy as np
X = data['Location']
y = data['Age']
# Label
print("Label Encoded")
le = LabelEncoder()
le.fit(X)
X_enc = le.transform(X)
X_train, X_test, y_train, y_test = train_test_split(X_enc, y, test_size = 0.20, random_state=42)
svclassifier = SVC(kernel='linear')
svclassifier.fit(np.reshape(X_train,(X_train.shape[0],1)), y_train)
y_pred = svclassifier.predict(np.reshape(X_test, (X_test.shape[0],1)))
print(f"y_pred: {y_pred}, y_test: {y_test}")
# OneHot
print("OneHot Encoded")
ohe = OneHotEncoder()
ohe.fit(np.reshape(X,(len(X),1)))
X_oh = ohe.transform(np.reshape(X,(len(X),1)))
X_train, X_test, y_train, y_test = train_test_split(X_oh, y, test_size = 0.20, random_state=42)
svclassifier = SVC(kernel='linear')
svclassifier.fit(X_train, y_train)
y_pred = svclassifier.predict(X_test)
print(f"y_pred: {y_pred}, y_test: {y_test}")
Дает:
Label Encoded
y_pred: [24 24], y_test: [24, 23]
OneHot Encoded
y_pred: [24 24], y_test: [24, 23]
Не так уж плохо.