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 […]
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
Forward selection is an iterative method in which we start with no function in the model. In each iteration, we add a function that best […]
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]: %matplotlib inline import numpy as np import matplotlib.pyplot as plt import […]
Jak wykorzystać PCA w regresji logistycznej?
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 %matplotlib […]
Cz. 2. Jak poprawić model klasyfikacyjny? Analiza głównych składowych (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 […]