What are the algorithms for big data mining?

1. Naive Bayes, super simple, just like doing some counting work. If the conditional independence hypothesis holds, NB will converge faster than the discriminant model, so you only need a small amount of training data. Even if the hypothesis of conditional independence is not established, NB still performs surprisingly well in practice.

2. Logistic regression, LR has many methods to regularize the model. Compared with NB's conditional independence hypothesis, LR does not need to consider whether the samples are relevant. Different from decision tree and support vector machine, NB has good probability interpretation ability, and it is easy to update the model with new training data. If you want some probability information or want to update and improve the model conveniently when there is more data in the future, LR is worth using.

3. Decision tree, DT is easy to understand and explain. DT is nonparametric, so there is no need to worry about whether outliers (or outliers) and data are linearly separable. The main disadvantage of DT is that it is easy to over-fit, which is also the reason why an ensemble learning algorithm such as random forest is proposed.

4. Support vector machine has high classification accuracy, which has a good theoretical guarantee for over-fitting. Facing the problem of inseparability of feature linearity, it can also perform well by choosing appropriate kernel function. SVM is very popular in high-dimensional text classification.

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