How do you decide what classifier to use?
Here are some important considerations while choosing an algorithm.
- Size of the training data. It is usually recommended to gather a good amount of data to get reliable predictions.
- Accuracy and/or Interpretability of the output.
- Speed or Training time.
- Linearity.
- Number of features.
What is the use of classifier?
A classifier utilizes some training data to understand how given input variables relate to the class. In this case, known spam and non-spam emails have to be used as the training data. When the classifier is trained accurately, it can be used to detect an unknown email.
What’s a good classification accuracy?
The most common metric used to evaluate the performance of a classification predictive model is classification accuracy. Typically, the accuracy of a predictive model is good (above 90% accuracy), therefore it is also very common to summarize the performance of a model in terms of the error rate of the model.
Which is classifier to choose, in general?
In nested cross validation, you perform cross validation on the model selection algorithm. Again, you first split your data into k folds. After each step, you choose k-1 as your training data and the remaining one as your test data. Then you run model selection (the procedure I explained above) for each possible combination of those k folds.
How many test items do I need for a classifier?
The classifier status is In progress while it processes the seed data. When the classifier is finished processing the seed data, the status changes to Need test items. You can now view the details page by choosing the classifier. Collect at least 200 test content items (10,000 max) for best results.
How long does it take to train a classifier?
Review the settings and choose Create trainable classifier. Within 24 hours the trainable classifier will process the seed data and build a prediction model. The classifier status is In progress while it processes the seed data. When the classifier is finished processing the seed data, the status changes to Need test items.
How to choose the best machine learning classifier?
Of course, if you really care about accuracy, your best bet is to test out a couple different ones (making sure to try different parameters within each algorithm as well), and select the best one by cross-validation.