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Table 1 Computational complexity of several commonly used regression and classification techniques

From: A data value metric for quantifying information content and utility

Classifier Type Training Prediction  
Linear Regression R \({O(p^{2} n + p^{3} )}\) \({O(p)}\) (4)
Decision Trees C&R \({O(n^{2} p)}\) \({O(p)}\)
Random Forest C \({O(n^{2} pk_{{trees}} )}\) \({O(pk_{{trees}} )}\)
Gradient Boosting C&R \({O(npk_{{trees}} )}\) \({O(pk_{{trees}} )}\)
SVM C&R \({O(n^{2} p + n^{3} )}\) \({O(m_{{sv}} p)}\)
k-Nearest Neighbors C&R varies \({O(np)}\)
Neural Networks C&R varies \({O(\sum _{i} o_{{l_{i} }} o_{{l_{{i + 1}} }} )}\)
Naive Bayes C \({O(np)}\) \({O(p)}\)