Using Random Search to Tune the Hyperparameters of a Random Decision Forest with Python

random search hyperparameter tuning a regression model python

Random search is an efficient method for automated hyperparameter tuning machine learning models. Hyperparameters are model properties (e.g., the number of estimators for an ensemble model). Unlike model parameters, the machine learning algorithm does not discover the model hyperparameters during training. Instead, we need to specify them in advance. Finding … Read more

Measuring Classification Performance with Python and Scikit-Learn

classification performance python confusion matrix roc curve

Classification is a supervised machine learning problem in which the task is to predict the correct class labels (two or more) for a set of observations. An essential step in developing a classifier is to evaluate its performance. Only when we understand how well a model sorts observations into the … Read more

Crime Prevention in San Francisco using XGBoost and Python

Crime Prediction using XGBoost and Python, sf crime map

This tutorial predicts crime types in San Francisco (SF) and plots them on a zoomable city map. We work with a Kaggle dataset containing past crimes and distinguish between 39 crime types, including vehicle theft, assault, and drug-related activities. We then use Python and Scikit-Learn to train a classification model … Read more

Customer Churn Prediction – Understanding Models with Feature Permutation Importance using Python

Churn prediction python tutorial

One of the primary goals of many service companies is to build solid and long-lasting relationships with their customers. Customers who churn cost a lot of money. Modern service companies who understand which customers tend to end the business relationship can take appropriate countermeasures early on to retain them. The … Read more

Hyperparameter Tuning a Random Forest Classifier using Grid Search in Python

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Hyperparameters control how a machine learning algorithm learns and how it behaves. Unlike the internal parameters (coefficients, etc.) that the algorithm automatically optimizes during model training, hyperparameters are model characteristics (e.g., the number of estimators for an ensemble model) that we must set in advance. Finding the optimal hyperparameter configuration … Read more