Category Archives: Supervised Learning

Synthetic Training Data Generation for Machine Learning Classification Problems using Ancestral Sampling

Access to good training data set is a serious impediment to building supervised Machine Learning models. Such data is scarce and when available, the quality of the data set may be questionable. Even if good quality data set is available, … 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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