How do you choose a classifier based on a training set size?
If the training set is small, high bias / low variance models (e.g. Naive Bayes) tend to perform better because they are less likely to overfit. If the training set is large, low bias / high variance models (e.g. Logistic Regression) tend to perform better because they can reflect more complex relationships.
What is classifier algorithm?
2 Classification algorithms. A classification algorithm, in general, is a function that weighs the input features so that the output separates one class into positive values and the other into negative values. To report the success of a classification algorithm, one must first have some metric of its performance.
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 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.
How is the fitting function used to train a classifier?
To train a classifier, the fitting function estimates the parameters of a Gaussian distribution for each class. Creates linear boundaries between classes. Creates nonlinear boundaries between classes (ellipse, parabola or hyperbola). Discriminant analysis in Classification Learner uses the fitcdiscr function.
Which is better high bias or low variance classifier?
If your training set is small, high bias/low variance classifiers (e.g., Naive Bayes) have an advantage over low bias/high variance classifiers (e.g., kNN), since the latter will overfit.