240120202201 In [67]: # Classification Assessment def Classification_Assessment(model ,Xtrain, ytrain, Xtest, ytest): import numpy as np import matplotlib.pyplot as plt from sklearn import metrics from sklearn.metrics […]
240420202112 Koty ¶ W 1]: class kolor_kota ( obiekt ): def __init__ ( self , imię , kolor ): self . imię = imię self […]
Perfect Plots: Bubble Plot Feel free to read the code on GitHub In [1]: import pandas as pd import matplotlib.pyplot as plt import numpy as […]
Feel free to read the code on GitHub Source of data: https://archive.ics.uci.edu/ml/datasets/combined+cycle+power+plant Combined Cycle Power Plant Data Set¶ Data Set Information:¶ The dataset contains 9568 […]
Feel free to read the code on GitHub In [1]: import joypy import pandas as pd import matplotlib.pyplot as plt Car market analysis¶ Source of […]
090420202257 https://github.com/slundberg/shap https://slundberg.github.io/shap/notebooks/NHANES%20I%20Survival%20Model.html In [1]: import pandas as pd df = pd.read_csv(’/home/wojciech/Pulpit/1/tit_train.csv’, na_values=”-1″) df.head(2) Out[1]: Unnamed: 0 PassengerId Survived Pclass Name Sex Age SibSp […]
090420201150 In [1]: import pandas as pd df = pd.read_csv(’/home/wojciech/Pulpit/1/tit_train.csv’, na_values=”-1″) df.head(2) Out[1]: Unnamed: 0 PassengerId Survived Pclass Name Sex Age SibSp Parch Ticket Fare […]
080420201050 practical use: predict_proba In [1]: import numpy as np import pandas as pd from sklearn.model_selection import train_test_split from sklearn.ensemble import RandomForestClassifier In [2]: from catboost.datasets import […]
060420202152 https://github.com/catboost/tutorials/blob/master/python_tutorial.ipynb CatBoostClassifier sam koduje sobie zmienne tekstowe kategoryczne na zmienne kategoryczne wyrażone numerycznie. Jeżeli sami przeprowadzimy codowanie i zakodujemy zmienne kategoryczne na format cyfrowy, […]
030420200928 In [1]: ## colorful prints def black(text): print(’33[30m’, text, ’33[0m’, sep=”) def red(text): print(’33[31m’, text, ’33[0m’, sep=”) def green(text): print(’33[32m’, text, ’33[0m’, sep=”) def […]
010420201017 Forward selection is an iterative method in which we start with no function in the model. In each iteration, we add a function that […]
300320202100 RFECV differs from Recursive Feature Elimination (RFE) in the function selection process in that it indicates the OPTIMAL NUMBER OF VARIABLES and not the […]
300320202027 Embedded methods are iterative in a sense that takes care of each iteration of the model training process and carefully extract those features which […]
300320201719 It is a greedy optimization algorithm which aims to find the best performing feature subset. It repeatedly creates models and keeps aside the best […]
300320201313 In backward elimination, we start with all the features and removes the least significant feature at each iteration which improves the performance of the […]
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