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Ensemble

Boosting: In lieu of training all the models separately as in bagging, boosting trains models sequentially. Each new model is trained to correct the errors made by the previous ones. The first tree is examined and the weights of those observations that are hard to classify are increased and the weights for those that are easy to classify are reduced. This modified data is used to build the next tree. This process is repeated for a defined number of iterations. Predictions of the final ensemble model is therefore the weighted sum of the predictions made by the previous tree models. GBM uses loss functions Since each tree is fit to residuals as against the original output parameter, each tree is small and improves prediction in the parts where prediction is bad. All the models might make the same mistake in the standard ensemble method. Compute error by deducting forecasted value from target value (e1= y - y1_forecasted) Build a new model on errors (e1_forecasted) as target variabl...
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Bias-Variance tradeoff

Bias refers to the deviation of the predicted values from the correct value. The error occurs when you make wrong assumptions about data. In other words, it is the error that is created when you represent a real-life complex problem using a simpler model while it might be making them easier to understand. For instance, when you build a linear model to solve for a non-linear problem. It results in under fitting and makes them less flexible. Parametric algos like Linear Regression can produce high bias while non-parametric algos like Decision Trees make good assumptions about the training data and target function and hence do not have high bias. Variance refers to the change that occurs when the model is applied on a different training data. It occurs when the model captures not just the underlying pattern but noise as well. It results in overfitting. In other words, it is memorising the data. It is often observed in Decision Trees. When the observations are limited but the...
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AI

we are now at a critical juncture where many of the systems we need to master are fiendishly complex, from climate change to macroeconomic issues to Alzheimer’s disease. The problem is that these challenges are so complex that even the world’s top scientists, clinicians and engineers can struggle to master all the intricacies necessary to make the breakthroughs required. It has been said that Leonardo da Vinci was perhaps the last person to have lived who understood the entire breadth of knowledge of their age. Since then we’ve had to specialise, and today it takes a lifetime to completely master even a single field such as astrophysics or quantum mechanics. The systems we now seek to understand are underpinned by a vast amount of data, usually highly dynamic, non-linear and with emergent properties that make it incredibly hard to find the structure and connections to reveal the insights hidden therein.  Kepler and Newton could write equations to describe the motion of p...
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