Hello Good People,
I have some label data and I am using the classification ML model (SVM, kNN) to train and test the dataset.
My input features are look likes:
(442, 443, 0.608923884514436), (444, 443, 0.6418604651162789)
The label is look likes:
Then I used
sklearn to train and test (after splitting the dataset 80% for train and 20% for the test). Code sample is given below:
classifiers = [ SVC(), KNeighborsClassifier(n_neighbors=5)] X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) trainingData = X_train trainingScores = y_train for item in classifiers: print(item) clf = item clf.fit(trainingData, trainingScores) y_pred = clf.predict(X_test) print("Accuracy Scor:") print(accuracy_score(y_pred, y_test)) print("Confusion Matrix:") print(confusion_matrix(y_pred, y_test)) print("Classification Report:") print(classification_report(y_pred, y_test))
The SVC Accuracy Score: 0.6639580602883355
The kNN Accuracy Score: 0.7171690694626475
I can guess, that the model is predicting some data correctly. Now, my question is,
- How can I save the prediction data including the label given by the model in a CSV file.
- Is it possible to use the
cross-validationconcept here? For example, if I want to apply 5 cross-validations. Then, how can I do that?
Though I am not sure, I am asking a freaking question or not (I am new in the ML fields).
Any kind of answer is appreciable.