Feature Selection Techniques – Embedded Method (Lasso)
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 […]
Feature Selection Techniques – Recursive Feature Elimination (RFE)
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 […]
Feature Selection Techniques – Backward Elimination
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 […]
Feature Selection Techniques [numerical result] – Step Forward Selection
300320201248 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 – Variance Inflation Factor (VIF)
290320202006 Collinearity is the state where two variables are highly correlated and contain similar information about the variance within a given dataset. The Variance Inflation […]
Feature Selection Techniques – Pearson correlation
290320201454 In [1]: import numpy as np import pandas as pd import seaborn as sns import matplotlib.pyplot as plt from sklearn.preprocessing import LabelEncoder, OneHotEncoder import warnings […]
Feature Selection Techniques (by filter methods): numerical_ input, categorical output
280320200940 Source of data: https://archive.ics.uci.edu/ml/datasets/Air+Quality In this case, statistical methods are used: We always have continuous and discrete variables in the data set. This procedure […]
Perfect Plot: Classification charts
Feel free to read the code on GitHub data source: https://archive.ics.uci.edu/ml/datasets/Air+Quality In [1]: import numpy as np import pandas as pd import seaborn as sns import […]
Feature Selection Techniques (by filter methods): categorical input, categorical output
categorical input – categorical output 260320201223 In this case, statistical methods are used: We always have continuous and discrete variables in the data set. This […]
Perfect model: Random forest classifier (1)
part 1: Determining the depth of trees by visualization using visualization¶ 230320201052 In [1]: import numpy as np import matplotlib.pyplot as plt import seaborn as […]
How to use PCA in logistic regression?
230320200907 Principal component analysis (PCA) https://jakevdp.github.io/PythonDataScienceHandbook/05.08-random-forests.html https://www.geeksforgeeks.org/principal-component-analysis-with-python/ In [1]: import pandas as pd import numpy as np import seaborn as sns import matplotlib.pyplot as plt df= […]
Part. 2 How to improve the classification model? Principal component analysis (PCA)
200320200904 In this case, the method did not improve the model. However, there are models in which the PCA method is a very important reason […]
Feature Selection Techniques – Random Forest Classifier
200320200724 In [1]: import pandas as pd df = pd.read_csv(’/home/wojciech/Pulpit/1/kaggletrain.csv’) df = df.dropna(how=’any’) df.dtypes Out[1]: Unnamed: 0 int64 PassengerId int64 Survived int64 Pclass int64 Name […]
Testy Kruskal -Wallis
https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.kruskal.html Unless you have a large sample size and can clearly demonstrate that your data are normal, you should routinely use Kruskal–Wallis; they think it […]
Part_7 Stroke_Prediction – Model Sieci neuronowych PyTorch Technika Osadzania
In [1]: import time start_time = time.time() ## pomiar czasu: start pomiaru czasu print(time.ctime()) Mon Mar 9 09:36:05 2020 In [2]: import torch import torch.nn as […]