7 common mistakes when doing Machine Learning
In statistical modeling, there are various algorithms to build a classifier, and each algorithm makes a different set of assumptions about the data. For Big Data, it pays off to analyze the data upfront and then design the modeling pipeline accordingly.
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4. Use high variance model when n<<p
SVM is one of the most popular offtheshelf modeling algorithms and one of its most powerful features is the ability to fit the model with different kernels. SVM kernels can be thought of as a way to automatically combine existing features to form a richer feature space. Since this power feature comes almost for free, most practitioners by default use kernel when training a SVM model. However, when the data has n<<p (number of samples << number of features)  common in industries like medical data  the richer feature space implies a much higher risk to overfit the data. In fact, high variance models should be avoided entirely when n<<p.
5. L1/L2/... regularization without standardization
Applying L1 or L2 to penalize large coefficients is a common way to regularize linear or logistic regression. However, many practitioners are not aware of the importance of standardizing features before applying those regularization.
Returning to fraud detection, imagine a linear regression model with a transaction amount feature. Without regularization, if the unit of transaction amount is in dollars, the fitted coefficient is going to be around 100 times larger than the fitted coefficient if the unit were in cents. With regularization, as the L1 / L2 penalize larger coefficient more, the transaction amount will get penalized more if the unit is in dollars. Hence, the regularization is biased and tend to penalize features in smaller scales. To mitigate the problem, standardize all the features and put them on equal footing as a preprocessing step.
6. Use linear model without considering multicollinear predictors
Imagine building a linear model with two variables X1 and X2 and suppose the ground truth model is Y=X1+X2. Ideally, if the data is observed with small amount of noise, the linear regression solution would recover the ground truth. However, if X1 and X2 are collinear, to most of the optimization algorithms' concerns, Y=2*X1, Y=3*X1X2 or Y=100*X199*X2 are all as good. The problem might not be detrimental as it doesn't bias the estimation. However, it does make the problem illconditioned and make the coefficient weight uninterpretable.
7. Interpreting absolute value of coefficients from linear or logistic regression as feature importance
Because many offtheshelf linear regressor returns pvalue for each coefficient, many practitioners believe that for linear models, the bigger the absolute value of the coefficient, the more important the corresponding feature is. This is rarely true as (a) changing the scale of the variable changes the absolute value of the coefficient (b) if features are multicollinear, coefficients can shift from one feature to others. Also, the more features the data set has, the more likely the features are multicollinear and the less reliable to interpret the feature importance by coefficients.
So there you go: 7 common mistakes when doing ML in practice. This list is not meant to be exhaustive but merely to provoke the reader to consider modeling assumptions that may not be applicable to the data at hand. To achieve the best model performance, it is important to pick the modeling algorithm that makes the most fitting assumptions  not just the one you’re most familiar with.
Original: Machine Learning Done Wrong
ChengTao Chu is a Director of Analytics at Codecademy. Specialties: data engineering and machine learning. Formerly: Google, LinkedIn and Square.
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