Category Archives: ScikitLearn

Building SciKitLearn Random Forest Model and Tuning Parameters without writing Python Code

Random Forest is a supervised learning algorithm which can be used for classification and regression. In this article we go though a process of training a Random Forest model including auto parameter tuning without writing any Python code.We will use … Continue reading

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Automated Machine Learning with Hyperopt and Scikitlearn without Writing Python Code

The most challenging part of building supervised machine learning model is optimization for algorithm selection, feature selection and algorithm specific hyper parameter value selection that yields the best performing model. Undertaking such a task manually is not feasible, unless the … Continue reading

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Supervised Machine Learning Parameter Search and Tuning with Simulated Annealing

The most challenging phase in supervised Machine Learning pipeline is parameter tuning. There are many parameters, each with a range of values. The so called grid search is brute force approach that tries all possible combinations of values for the … Continue reading

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Auto Training and Parameter Tuning for a ScikitLearn based Model for Leads Conversion Prediction

This is a sequel to my last blog on CRM leads conversion prediction using Gradient Boosted Trees as implemented in ScikitLearn. The focus of this blog is automatic training and parameter tuning for the model. The implementation is available in … Continue reading

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Predicting CRM Lead Conversion with Gradient Boosting using ScikitLearn

Sales leads are are generally managed and nurtured in CRM systems. It will be nice if we could predict the likelihood of any lead converting to an actual deal. This could be very beneficial in many ways e.g. proactively  providing … Continue reading

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