Search results for "MinMaxScaler"

Box Plot of PCA Number of Components vs. Classification Accuracy

Principal Component Analysis for Dimensionality Reduction in Python

Reducing the number of input variables for a predictive model is referred to as dimensionality reduction. Fewer input variables can result in a simpler predictive model that may have better performance when making predictions on new data. Perhaps the most popular technique for dimensionality reduction in machine learning is Principal Component Analysis, or PCA for […]

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Histogram Plots of the Variables for the Phoneme Dataset

Predictive Model for the Phoneme Imbalanced Classification Dataset

Many binary classification tasks do not have an equal number of examples from each class, e.g. the class distribution is skewed or imbalanced. Nevertheless, accuracy is equally important in both classes. An example is the classification of vowel sounds from European languages as either nasal or oral on speech recognition where there are many more […]

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Histogram of Each Variable in the Oil Spill Dataset

How to Develop an Imbalanced Classification Model to Detect Oil Spills

Many imbalanced classification tasks require a skillful model that predicts a crisp class label, where both classes are equally important. An example of an imbalanced classification problem where a class label is required and both classes are equally important is the detection of oil spills or slicks in satellite images. The detection of a spill […]

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Box and Whisker Plot of Probabilistic Models on the Haberman Breast Cancer Survival Dataset

How to Develop a Probabilistic Model of Breast Cancer Patient Survival

Developing a probabilistic model is challenging in general, although it is made more so when there is skew in the distribution of cases, referred to as an imbalanced dataset. The Haberman Dataset describes the five year or greater survival of breast cancer patient patients in the 1950s and 1960s and mostly contains patients that survive. […]

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Results for Standard Classification and Regression Machine Learning Datasets

Best Results for Standard Machine Learning Datasets

It is important that beginner machine learning practitioners practice on small real-world datasets. So-called standard machine learning datasets contain actual observations, fit into memory, and are well studied and well understood. As such, they can be used by beginner practitioners to quickly test, explore, and practice data preparation and modeling techniques. A practitioner can confirm […]

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How to Transform Target Variables for Regression With Scikit-Learn

How to Transform Target Variables for Regression in Python

Data preparation is a big part of applied machine learning. Correctly preparing your training data can mean the difference between mediocre and extraordinary results, even with very simple linear algorithms. Performing data preparation operations, such as scaling, is relatively straightforward for input variables and has been made routine in Python via the Pipeline scikit-learn class. […]

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