Linear discriminant analysis (LDA) is an approach used in supervised machine learning to solve multi-class classification problems. LDA separates multiple classes with multiple features through data dimensionality reduction. This technique is important in data science as it helps optimize machine learning ...
The previous table is just a representation. In reality, the categorical values start from 0 goes all the way up to N-1 categories. As you probably already know, the categorical value assignment can be done usingsklearn’s LabelEncoder. ...
Bagging is anensemble learningstrategy that helps machine learning algorithms increase their performance and accuracy. It is used to cope with bias-variance trade-offs and decreases a prediction model’s variance. Bagging is also known asbootstrap aggregation. Out-of-bag (OOB) observations are not ...
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The table below shows a subset of the whole training dataset. Features X0-X8 are categorical variables and we have to transform them into in a form that is useable by our model – numerical values. This was done usingscikit-learn’sLabelEncoder class. ...
from sklearn.preprocessing import StandardScaler, LabelEncoder from sklearn.model_selection import train_test_split from sklearn.discriminant_analysis import LinearDiscriminantAnalysis from sklearn.ensemble import RandomForestClassifier from sklearn.metrics import accuracy_score, confusion_matrix ...