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Bayesian classifier for text classification Text classification is not only a large module in modern machine learning applications, but also one of the foundations of natural language processing. We can process text data into digital data, and then use Bayes to help us judge a paragraph, or the topic classification, emotional tendency, and eve ...

summary SVM was first proposed by Vladimir N. Vapnik and Alexey Ya. Chervonenkis in 1963. The current version (soft margin) was proposed by Corinna Cortes and Vapnik in 1993 and published in 1995. Before the emergence of deep learning (2012), SVM was considered as the most successful and best performing algorithm in machine learning in rec ...

The main modules used in this analysis are: numpy, matplotlib, pandas, sklearn.liner_ Lineexpression in model   1. Data reading The data includes user behavior data and vip user behavior data 2. Data analysis When we use user_behavior_data.isna().count() After finding that there are no missing values in the code, we start to analyze th ...

SVM is a very elegant algorithm with perfect mathematical theory. It is often used in data classification and data regression prediction. Due to its beautiful theoretical guarantee and the processing skills of kernel function for linear inseparable problems, SVM was very popular around the 1990s. SVM includes the functions of many algorithms: ...

sklearn -- Transformer and estimator Among the sklearn I contacted before, there are SimpleImputer, OrdinalEncoder and OneHotEncoder. What they have in common is that they are all used for data feature preprocessing. In the use of them, it is inevitable to encounter: fit_transform,transform. At first, I didn't fully understand it, but I ...

Experimental introduction 1. Experimental contents This experiment learns and implements the decision tree algorithm. 2. Experimental objectives Through this experiment, master the basic principle of decision tree algorithm. 3. Experimental knowledge points Shannon entropyinformation gain 4. Experimental environment python 3.6.5 5. Pr ...

preface python final exam requires a feature extraction project. Today I'll show my results 1, Code import numpy as np # Linear discriminant analysis is imported from sklearn's linear analysis library, that is, LDA maximizes the coordinate axis of inter class discrimination to reduce the dimension of classification preprocessing from sklear ...

Principle of Kmeans clustering method: 1. Firstly, K clustering centers are randomly determined; 2. Calculate the distance (Euclidean distance) from each point in the data to the K cluster centers, and classify the point into which cluster which is the smallest; 3. Calculate the center points of all points in each cluster (the corresponding ele ...

·Introduction to Word2Vec         One of the core concepts of natural language processing is how to quantify words and expressions so that they can be used in the model environment. This mapping of language elements to numerical representations is called word embedding.         Word2Vec is a word ...

Iris data set is a classic small-scale data set in machine learning. Through the following experiments, access to materials and videos for learning, share the learning experience and experimental process of the whole experiment, hoping to provide help to novices who love machine learning and get started, and urge themselves to move forward stea ...