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Because of: The (extreme) dominance of noise over signal The prevalence of non-repeating patterns (many of which we know are not going to repeat) A pathetic sample size for cross-validation Regime changes due to exogenous events. These are typically in the cross-val window which makes it even worse. (GFC, financial integration, trade law changes, interest ...


3

As with many machine learning technologies, you can run a separate training and testing phase before deploying it live for prediction. All it does is build a collection of decision trees based on the parameters you give it - if the output field is a factor, you get classification (a finite enumerated set of values); if it's numeric, you get prediction. One ...


3

I was going to comment but it turned out to be quite elaborate. My experience with certain AI/ML methods is that they're not deterministic. Take RBM for instance, a very wide-spread paradigm. To train such a machine you have two approaches, backpropagation or Kullback-Leibler divergence. Both require you to initialise the machine to a random state. And ...


2

Scale and range are your biggest issues. If one input has values which range from e.g. 2300-3500, and another from 0 to 18, then the large scale of the first will swamp the other and provide greater informativity into your learning algorithm. Therefore, normalize into range [0,1] or mean-zero standardize - like you have already done. Be careful with ...


2

A while ago I have implemented a binary fuzzy decision tree forest to classify credit applications as a semesters project. Let's say a tree looks like this: C1 C11 -> X -> Y C12 C121 -> A -> B -> U The benefits of decision tree techniques in general are: Comprehensibility: The paths down ...


2

It's probably because of the strong long-standing statistical underpinnings in economics and econometrics, and overall, risk prediction. For example, look at current research with fat-tail distributions and calculations for Expected Tail Loss (ETL), etc. These studies fit Student's t, Normal, Stable, and Pareto probability distributions to data and report ...


1

There can be several reasons for this: The "new data" that you use post-training & post-validation is not drawn from the same distribution as the one that you used to create/draw your training, testing and validation data. Since you have not mentioned anything related to the input features in your data-set, I am assuming that the ...


1

A classifier can be weak for a number of reasons, and it mainly depends on characteristics of the data. For example, if the data are not linearly separable, then linear regression will be weak (poor correlation between predicted class and true class labels). However, if the data are linearly separable, then other classifiers may not work as well as linear ...



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