What are the classification algorithms used for data mining and what are the advantages and disadvantages of each?

1. Naive Bayes (NB)

Simple, like doing some counting.

If the conditional independence assumption holds, NB will converge faster than discriminative models such as logistic regression, so you only need a small amount of training data.

If you want to do something like semi-supervised learning, or if you want a model that is both simple and performs well, NB is worth trying.

2.?Logistic Regression (LR)

LR has a number of ways to regularize the model. Compared to NB's conditional independence assumption, LR does not need to consider whether the samples are correlated.

If you want some probabilistic information (e.g., to adjust classification thresholds more easily, to get classification uncertainty, to get confidence intervals), or you want to improve the model by easily updating it in the future when more data are available, LR is worth using.

3. Decision Tree (DT)

DT is non-parametric, so you don't need to worry about wild points (or outliers) and whether the data is linearly separable (e.g., DT easily handles situations where samples belonging to class A tend to have very small or large values of feature x, while samples belonging to class B have very small or large values of feature x). samples belonging to class A tend to have very small or very large values of feature x, while samples belonging to class B have values of feature x in the middle range).

The main disadvantage of DT is that it is prone to overfitting, which is precisely why integrated learning algorithms such as Random Forest (RF) (or Boosted Tree) have been proposed.

In addition, RF often performs best in many classification problems, is fast and scalable, and does not require a large number of parameters to be tuned like SVMs do, which is why RF is a very popular algorithm these days.

4. Support Vector Machine (SVM)

High classification correctness, good theoretical guarantees against overfitting, selecting the appropriate kernel function, the face of the problem of linearly indistinguishable features can also perform well.

SVMs are very popular for text categorization where the dimensionality is usually high. I think RF is starting to threaten its position due to its large memory requirements and cumbersome tuning.