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…l and so on. Instead, my first question is always: “Can we get more data relevant to the problem?”. As Geoff Hinton (the father of deep neural networks) has pointed out in an article titled ‘The Unreasonable Effectiveness of Data”, the amount of useful data is more important to the problem than the complexity of the model. Others have echoed the idea that a simple model and plenty of data will beat a complex model with limited data. If there is more information that can help with our problem that we are not using, the best payback…

…andom subset of the data points with replacement (called bagging, short for bootstrap aggregating). (We can turn off the sampling with replacement and use all the data points by setting bootstrap = False when making the forest). Random sampling of data points, combined with random sampling of a subset of the features at each n…

…own to the left and on to the third and final question which is True as well because temp_1 ≤ 44.5. Therefore, we conclude that our estimate for the maximum temperature is 41.0 degrees as indicated by the value in the leaf node. An interesting observation is that in the root node, there are only 162 samples despite there being 261 training data points. This is because each tree in the forest is trained on a random subset of the data points with replacement (called bagging, short for bootstrap aggregating). (We can turn off the sampling with replacement and use all the data points by setting bootstrap = F…