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In the previous article, the data set is divided into 37 points in order, which will lead to inaccurate results. Therefore, this paper uses the kfold method in sklearn to realize cross validation, so as to make the results more accurate Last article ----- > Python post processing data format run model (pycruise) - verify data validity ...

Super parameter optimization methods: grid search, random search, Bayesian Optimization (BO) and other algorithms. ​ reference material: Three super parameter optimization methods are explained in detail, as well as code implementation ​ Experimental basic code import numpy as np import pandas as pd from lightgbm.sklearn import LGBMRegr ...

Characteristic Engineering 1. Definitions 1.1 why feature engineering is needed The features in the sample data may have missing values, duplicate values, abnormal values, etc., so the relevant noise data in the features need to be processed Processing purpose: there is a purer sample set, so that the model can have better prediction abil ...

1. Feature digitization 1.1 Replace() function import pandas as pd df = pd.DataFrame({"gene_segA": [1, 0, 0, 1, 1, 1, 0, 0, 1, 0], "gene_segB": [1, 0, 1, 0, 1, 1, 0, 0, 1, 0], "hypertension": ["Y", 'N', 'N', 'N', 'N', 'N', 'Y', 'N', 'Y', 'N'], "Gallstones": ['Y', 'N', 'N', 'N', 'Y', 'Y', 'Y', 'N', 'N', 'Y'] }) df df.replace({"N": 0, 'Y': ...

Scikit learn nanny level introductory tutorial Scikit learn is a well-known Python machine learning library, which is widely used in data science fields such as statistical analysis and machine learning modeling. Modeling invincible: users can realize various supervised and unsupervised learning models through scikit learnVarious functions: a ...

Measuring the performance of clustering algorithm is not simply counting the number of errors or calculating the precision and recall in supervised classification algorithm. There are many evaluation indicators for clustering algorithms. This paper is mainly based on the sklearn machine learning library, which provides a series of measu ...

catalogue 1 integrated learning 2 random forest classifier 2.1 random forest classifier function and its parameters 2.2 construction of random forest 2.3 comparison of random forest and decision tree under cross validation 2.4 drawing n_ Learning curve of estimators 3 random forest regressor 3.1 random forest classifier function and its ...

1. Principal Component Analysis (PCA) Thought and Principle 1.1 What is principal component analysis PCA(Principal Component Analysis), a principal component analysis method, is the most widely used data dimension reduction algorithm (unsupervised machine learning method). Its main purpose is to "reduce dimensionality", by disjunct ...

Feature extraction is simply to convert a series of data into digital features that can be used for machine learning. sklearn.feature_extraction is a module of scikit learn feature extraction This paper summarizes the following contents: Onehot encodingDictVectorizer usesCountVectorizer useTfidfVectorizer usesHashingVectorizer uses 1.O ...

Rimeng Society AI AI:Keras PyTorch MXNet TensorFlow PaddlePaddle deep learning real combat (irregular update) Integrated learning: Bagging, random forest, Boosting, GBDT Integrated learning: XGBoost Integrated learning: lightGBM (I) Integrated learning: lightGBM (II) 5.1 principle of xgboost algorithm XGBoost (Extreme Gradient Boosting ...