Category: machine learning

machine learning

Python structure for machine learning experiments

Here we will present the setup for single machine to run time consuming machine learning experiments like feature selection using different machine learning models. First we will create python program which runs single experiment. We will use argparse library to

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Feature selection

Feature selection in low signal-to-noise environments like finance. In the following we will create a feature selection function which would work on XGBoost models as well as Tensorflow and simple sklearn models. We will use univariate as well as other

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How to check time-series for abnormality

In many time series machine learning problems the with large number of features the raw data might contain – abnormal / extreme points – discontinuities – stale data To help with determining quickly abnormal or extreme points we can use

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How to add feature interactions

Sometimes it is preferable to use simple machine learning algorithms such as logistic regression due to speed and explainability. But usually these simple algorithms do not incorporate interactions of the features (in contrary to , say, neural networks, where sum/difference

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time series feature transformations – quantiles

For machine learning algorithms to work well, it’s usually useful to remove noise from features.For time-series this can be achieved in several ways, such as moving averages, applying sign transform, or applying low pass filter. Other, more simple way is

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How to run sklearn’s GridSearchCV with Tensorflow keras models.

To find optimal parameters for Neural network one would usually use RandomizedSearchCV or GridSearchCV from sklearn library. Tensorflow keras models, such as KerasClassifier, when calling fit() function does not permit to have different number of neurons. GridSearchCV and RandomizedSearchCV call

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