Article
In this guide, we will focus on implementing the Hierarchical Clustering Algorithm with Scikit-Learn to solve a marketing problem. After reading the guide, you will understand: When to apply Hierarchical Clustering How to visualize the dataset to understand if it is fit for clustering How to pre-process features and engineer...
Cássia Sampaio
Byte
Regression is a technique in statistics and machine learning, in which the value of an independent variable is predicted by its relationship with other variables. Frameworks like Scikit-Learn make it easier than ever to perform regression with a wide variety of models - one of the strongest ones being built...
David Landup
Regression is a technique in statistics and machine learning, in which the value of an independent variable is predicted by its relationship with other variables. Frameworks like Scikit-Learn and XGBoost make it easier than ever to perform regression with a wide variety of models - one of the recently well-adopted...
Scikit-Learn's scalers are the backbone of practically all regressors and classifiers built on top of them, scaling the data to a workable range and preparing a latent representation to learn from. If you'd like to read more about feature scaling, read our "Feature Scaling Data with Scikit-Learn for Machine...
RandomizedSearchCV and GridSearchCV allow you to perform hyperparameter tuning with Scikit-Learn, where the former searches randomly through some configurations (dictated by n_iter) while the latter searches through all of them. XGBoost is an increasingly dominant library, whose regressors and classifiers are doing wonders over more traditional implementations, and is...
XGBoost is gaining a lot of traction, and its downloads are increasing. Yet, there's a common issue with the installation, especially in Jupyter Notebook environments where it's typically installed with: ! pip install xgboost # Or ! pip3 install xgboost # Or ! conda install -c conda-forge xgboost Oftentimes, even though this approach works for...
Saving and loading Scikit-Learn models is part of the lifecycle of most models - typically, you'll train them in one runtime and serve them in another. In this Byte - you'll learn how to save and load a regressor using Scikit-Learn. First off, let's build a simple regressor and fit...
So - you've trained a sparkling regressor using XGBoost! Which features are the most important in the regression calculation? The first step in unboxing the black-box system that a machine learning model can be is to inspect the features and their importance in the regression. Let's quickly train a mock...
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