Pytorch regression 2.3 [AirQualityUCI.csv]
020520201302 Work on diagnostic systems. There is no progress in tank construction without diagnostics. In [1]: import torch I’m starting a GPU graphics card (which I […]
Pytorch regression _2.1_ [WorldHappinessReport.csv]
300420201044 https://github.com/jcjohnson/pytorch-examples#pytorch-custom-nn-modules In [1]: import torch I’m starting a GPU graphics card (which I don’t have) Odpalam karte graficzną GPU (której nie mam) In [2]: device = […]
Pytorch regression _1.1_[WorldHappinessReport]
290420201753. Tworzenie małych prototypów o pełnej zdolności bojowej Cele: 1. podstawić prawdziwy plik danych 2. przeliczyć zapamiętać model odpalić model 3. zweryfikowac model czy liczy […]
Review of models based on gradient falling: XGBoost, LightGBM, CatBoost
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 […]
Kilka prostych przykładów z programowanie objektowe w Python
240420202112 Koty ¶ W 1]: class kolor_kota ( obiekt ): def __init__ ( self , imię , kolor ): self . imię = imię self […]
Perfect Plots Bubble Plot
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 […]
Perfect Plots_ Matrix of corelation
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 […]
Perfect plot Joyplot
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 […]
Interpretation of SHAP charts for the Titanic case (Feature Selection Techniques)
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 […]
Homemade loop to search for the best functions for the regression model (Feature Selection Techniques)
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 […]
How to calculate the probability of survival of the Titanic catastrophe_080420201050
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 […]
CatBoost Step 1. CatBoostClassifier (cat_features)
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 […]
Feature Selection Techniques [categorical result] – Step Forward Selection
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 […]
Feature Selection Techniques – Recursive Feature Elimination and cross-validated selection (RFECV)
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 […]