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1: Introduction         In the last article, we have a relative understanding of knn algorithm, and roughly understand the working principle and workflow of knn algorithm through a very simple example. This article will continue to analyze the knn Algorithm in depth to help understand the knn algorithm 2: Algorithm anal ...

Recently, I participated in the learning activity of 'Coggle data science 30 Days of ML', and recorded my notes here. The activity is completely free and feels like a good opportunity for promotion. The publicity pictures of the event are as follows: All right, let's get to the point Import package import pandas as pd import numpy as np imp ...

Tensor mathematical operations mainly include scalar operations, vector operations and matrix operations. In addition, we will introduce the broadcast mechanism of tensor operation. 1. Scalar operation The mathematical operators of tensors can be divided into scalar operators, vector operators and matrix operators. Addition, subtraction, ...

I. First Identity HMM Hidden Markov Model (HMM) is a statistical model used to describe the hidden unknown parameters. HMM has been successfully used in speech recognition, text classification, bioinformatics, fault diagnosis and life prediction. HMM can be composed of three elements:= (A,B,II), where A is the state transition probability mat ...

1 Introduction to causalnex 1.1 installation pip install causalnex github:https://github.com/quantumblacklabs/causalnex Document link: https://causalnex.readthedocs.io/en/latest/02_getting_started/02_install.html CausalNex is a Python library that uses Bayesian networks to combine machine learning and domain expertise for causal re ...

Machine learning process record 1 # np.meshgrid generates grid point coordinate matrix. Turn the plan into a 'net map' to facilitate the overall coloring in the back # The NP. Range() function returns a fixed step arrangement with an end point and a start point xx, yy = np.meshgrid(np.arange(x_min, x_max, .02), np.arange( ...

1 Preface 1.1 data set source The data in this case comes from the real data of Toronto in 2018-2019 on Airbnb website.The data set contains the listing data set, with about 20000 pieces of data, recording all the house information, including dozens of information fields including price.Another data set in the data set is calendar, which ...

A * preliminary detailed explanation of algorithm Please see Baidu Encyclopedia for details The core valuation function: f(x) = g(x) + h(x) g(x) is the distance function from the starting point, such as how many steps have been taken from the starting point.h(x) is the heuristic search function for the current position to reach the end ...

data Jack cherish notes Machine learning practice kNN step 1 calculate the distance between the unknown point and the sample 2 distance increasing sort 3 select the first K samples according to the sorting results 4 determine the occurrence frequency of the first k samples 5 the category of the point with the highest frequency is the re ...

1. Ridge regression model 1.1 background For regression problems, their basic contents are basically the same, so ridge regression model and linear regression model similar: y = θ 0 ...