choosing machine learning classifier

The task of supervised learning in machine learning focuses on predicting the target/marker of an unknown sample based on existing empirical knowledge.According to the different types of target predictor variables, we divide the task of supervised learning into two categories: Classification

Absolute Percent error average absolute percent errors), defined as follows:Compared with Rmse,mape, the error of each point is normalized, eliminating the effect of absolute error caused by individual outliers.Summary and extensionIn this article, we are based on three hypothetical Hulu scenarios, mainly explaining the importance of evaluating the choice of indicators. Each evaluation indicator has its value, but if the model is evaluated only from a single evaluation index, it often results i

This blog is reproduced from a blog post, introduced Gan (generative adversarial Networks) that is the principle of generative warfare network and Gan's advantages and disadvantages of analysis and the development of GAN Network research. Here is the content. 1. Build Model 1.1 Overview Machine learning methods can be divided into generation methods (generative approach) and discriminant methods (discrimin

(such as GBDT) are typical of the method, today mainly talk about the gradient boosting method (this is a little different from the traditional boosting) some mathematical basis, With this mathematical basis, the application above can be seen Freidman gradient boosting machine.This article requires the reader to learn basic college mathematics, as well as the basic machine learning concepts of classificati

That years. I learn the main contents of machine learning:1. Basic introduction to machine learning, getting started with machine learning; 2. Linear regression and logistic. XX Performance Prediction System. Intelligent interacti

Part IV Generation Learning Algorithm So far, we have largely discussed the learning Algorithm model: P (y|x;θ), given x, the conditional probability distribution of Y. For example, the logistic regression model: P (y|x;θ), Where: Here the function g is a sigmoid function. In this article, we will discuss another type of learning algorithm. Consider a classific

Neural Networks are getting angry again. Because deep learning is getting angry, we must add a traditional neural network introduction, especially the back propagation algorithm. It is very simple, so it is not complicated to say anything about it. The neural network model is shown in Figure 1: (Figure 1) (Figure 1) the neural network model in is composed of multiple perceptron layers. The sensor is a single-layer Neural Network (accurate, it should

, the use of very convenient, greatly reduced the application of machine learning threshold. Of course, the shortcomings are obvious, because of the UDF programming interface provided by the database, the implementation of the algorithm will be subject to a lot of constraints, many optimizations difficult to achieve, and large-scale data sets of machine

are extracted, such as decision tree, Bayesian classifier, and SVM. The following describes various typical machine learning methods: 1. Decision Tree The decision tree design is like this. The root node is set based on the most obvious property features of the sample. The branches represent the attribute values, and the leaf nodes represent the classifica

Summary:Classification and Regression tree (CART) is an important machine learning algorithm that can be used to create a classification tree (classification trees) or to create a regression tree (Regression tree). This paper introduces the principle of cart used for discrete label classification decision and continuous feature regression. The decision tree creation process analyzes the information Chaos Me

ProfileThe commonly used machine learning algorithms:\ (k\)-Nearest neighbor algorithm, decision tree, naive Bayesian,\ (k\)-mean clustering its ideas and Python code implementation summary. Do not have to know it but also know the reason why. Refer to "machine learning combat".? ?\ (k\)-Nearest Neighbor algorith

**************************************Note: This blog series is for bloggers to learn the "machine learning" course notes from Professor Andrew Ng of Stanford University. Bloggers deeply learned the course, do not summarize is easy to forget, according to the course plus their own to do not understand the problem of the addition of this series of blogs. This blog series includes linear regression, logistic

logistic regression, the difference is that the learning model function hθ (x) is different, the specific solution process of the gradient method is "the specific explanation of machine learning classical algorithm and the implementation of Python---logistic regression (LR) classifier".2,normal equation (also known as