I try to fit SVC in skikit-learn, but got TypeError: fit() missing 1 required positional argument: 'self' in the line SVC.fit(X=Xtrain, y=ytrain)
from sklearn.svm import SVC
import seaborn as sns; sns.set()
from sklearn.datasets.samples_generator import make_circles
from sklearn.model_selection import train_test_split
from sklearn.model_selection import cross_val_score
X, y = make_circles(100, factor=.2, noise=.2)
Xtrain, Xtest, ytrain, ytest = train_test_split(X,y,random_state=42)
svc = SVC(kernel = "poly")
SVC.fit(X=Xtrain, y=ytrain)
predictions = SVC.predict(ytest)
The problem is that you are creating the model here svc = SVC(kernel = "poly"), but you're calling the fit with a non-instantiable model.
You must change the object to:
svc_model = SVC(kernel = "poly")
svc_model.fit(X=Xtrain, y=ytrain)
predictions = svc_model.predict(Xtest)
I suggest you to Indique the test size, normally the best practice is with 30% for test and 70% for training. So you can indicate.
Xtrain, Xtest, ytrain, ytest = train_test_split(X,y,test_size=0.30, random_state=42)
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