The question proposed reads as follows: Use scikit-learn to split the data into a training and test set. Classify the data as either cat or dog using DBSCAN.
I am trying to figure out how to go about using DBSCAN to fit a model using training data and then predict the labels of a testing set. I am well aware that DBSCAN is meant for clustering and not prediction. I have also looked at Use sklearn DBSCAN model to classify new entries as well as numerous other threads. DBSCAN only comes with fit and fit_predict functions, which don't seem relatively useful when trying to fit the model using the training data and then test the model using the testing data.
Is the question worded poorly or am I missing something? I have looked at the scikit-learn documentation as well as looked for examples, but have not had any luck.
# Split the samples into two subsets, use one for training and the other for testing
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)
# Instantiate the learning model
dbscan = DBSCAN()
# Fit the model
dbscan.fit(X_train, y_train)
# Predict the response
# Confusion matrix and quantitative metrics
print("The confusion matrix is: " + np.str(confusion_matrix(y_test, dbscan_pred)))
print("The accuracy score is: " + np.str(accuracy_score(y_test, dbscan_pred)))
Whoever gave you that assignment has no clue...
DBSCAN will never predict "cat" or "dog". It just can't.
Because it is an unsupervised algorithm, it doesn't use training labels. y_train is ignored (see the parameter documentation), and it is stupid that sklearn will allow you to pass it at all! It will output sets of points that are clusters. Many tools will enumerate these sets as 1, 2, ... But it won't name a set "dogs".
Furthermore it can't predict on new data either - which you need for predicting on "test" data. So it can't work with a train-test split, but that does not really matter because it does not use labels anyway.