1. Linear relationship model
A function that predicts through a linear combination of attributes:
w is the weight, b is the offset term, which can be understood as:
2. Linear regression (iteration, self-learning)
Definition: linear regression is a regression analysis modeled between one or more independent variables and dependent v ...
Posted by davidz on Wed, 01 Dec 2021 15:44:24 -0800
Sample imbalance in binary SVC: an important parameter class_weight
For the classification problem, one of the pain points that can never escape is the sample imbalance problem. Sample imbalance refers to a class of labels in a set of data sets It accounts for a large proportion, but we have the situation to capture the needs of a speci ...
Posted by ukalpa on Wed, 01 Dec 2021 05:29:51 -0800
Bayesian classifier is a probability model, which uses Bayesian formula to solve the classification problem. Assuming that the feature vector of the sample obeys a certain probability distribution, we can calculate the conditional probability that the feature vector belongs to each class. The classification result is the one with the l ...
Posted by jtravis on Tue, 30 Nov 2021 17:46:05 -0800
Hello, everyone. Today I'd like to share with you the logical regression algorithm in python machine learning. The main contents include:
(1) Algorithm principle; (2) Accuracy and recall; (3) Case application -- cancer case prediction.
At the end of the paper, there are data sets and complete python code
1. Concept understanding
Logistic r ...
Reference link: mainly based on the Chinese version of scikit learn (sklearn) official documents: https://sklearn.apachecn.org/#/ 7 text feature extraction methods: http://blog.sina.com.cn/s/blog_b8effd230102zu8f.html Train of sklearn_ test_ Explanation of the meaning of the parameters of split() function (very complete): https://www.cnblogs.co ...
Posted by angrytuna on Wed, 10 Nov 2021 22:53:07 -0800
Source: Data STUDIO
Author: Yun Duojun
1. Brier Score
The accuracy of probability prediction is called "calibration degree", which is a way to measure the difference between the probability predicted by the algorithm and the real result. A commonly used indicator is called Brill score, which is calculated as the mean square err ...
Posted by tomz0r on Thu, 28 Oct 2021 02:00:07 -0700
In this experiment, we will use support vector machine (SVM) and understand its working principle on data.
The data sets used in this experiment include:
ex2data1.mat - linear SVM classification datasetex2data2.mat - Gaussian kernel SVM classification datasetex2data3.mat - cross validation Gaussian kernel SVM classification dat ...
Posted by egturnkey on Fri, 15 Oct 2021 11:55:14 -0700
linear regression model The univariate linear regression model uses a single feature to predict the response value, and the best fitted curve is obtained by minimizing the error between the predicted value and the real value.
Multiple regression model uses multiple independent variables to estimate dependent variables, so as to explain and pr ...
Posted by sklein99 on Mon, 04 Oct 2021 13:51:20 -0700
For the implementation of python code of basic machine learning algorithm, please refer to:
knn is a very basic algorithm in the field of machine learning, which can solve classification or regression problems. If it is just beginning to learn machine learning, knn is a very good entry choice. It ...
Posted by maxkbr on Sun, 03 Oct 2021 15:06:37 -0700
Decision tree in sklearn
tree.DecisionTreeClassifierClassification treetree.DecisionTreeRegressorRegression treetree.export_graphvizThe generated decision tree is exported to DOT mode for drawingtree.ExtraTreeClassifierHigh random version classification treetree.ExtraTreeRegressorHigh random version of regression tr ...
Posted by WebMonkey on Wed, 22 Sep 2021 19:15:25 -0700