How do I create an array or dataframe to store seedN, clf.score(X_test, y_test)
,n_neighbors
?
from sklearn.model_selection import train_test_split for seedN in range(1,50,1): X_train, X_test, y_train, y_test = train_test_split(indicators,data2['target'], test_size=0.25, random_state=seedN) training_accuracy = [] test_accuracy = [] neighbors_settings = range(1, 70) # try n_neighbors from 1 to 50 for n_neighbors in neighbors_settings: clf = KNeighborsClassifier(n_neighbors=n_neighbors) # build the model clf.fit(X_train, y_train) training_accuracy.append(clf.score(X_train, y_train)) # record training set accuracy test_accuracy.append(clf.score(X_test, y_test)) # record generalization accuracy
Advertisement
Answer
Create a temporary empty list to store the results :
tmp = []
For each fit, add a new list with the desired values :
for seedN in range(1, 50, 1): # your code for n_neighbors in neighbors_settings: # your code tmp.append([seedN, clf.score(X_test, y_test), n_neighbors])
Finally, create the dataframe with this temporary list :
df = pd.DataFrame(tmp, columns=["seedN", "score", "n_neighbors"])