Menu
I'm looking into ML problems (mostly density estimation and anomaly detection) with paths made up of coordinates (GPS). Other than the coordinates themselves and deltas (changes between adjacent coordinate points) and polar coordinates what are some other good features? What features make intuitive attributes like straightness, curvy-ness, smoothness, and loopy-ness explicit?
355
There are three main goals to feature selection. Improve the accuracy with which the model is able to predict for new data. Reduce computational cost. Produce a more interpretable model.
Machine learning is a field of computer science that uses statistical techniques to give computer programs the ability to learn from past experiences and improve how they perform specific tasks. With this learning path, you'll sample a range of common machine learning scenarios using Python.
In machine learning and pattern recognition, a feature is an individual measurable property or characteristic of a phenomenon. Choosing informative, discriminating and independent features is a crucial element of effective algorithms in pattern recognition, classification and regression.
Exhaustive Feature Selection- Exhaustive feature selection is one of the best feature selection methods, which evaluates each feature set as brute-force. It means this method tries & make each possible combination of features and return the best performing feature set.
For straightness/curviness you may want to calculate an approximate first derivative of the curve, for smoothness the second and higher derivatives. If by loopiness you mean the tendency to return to places several times, you could for instance count how many segments intersect each other.
144
If you love us? You can donate to us via Paypal or buy me a coffee so we can maintain and grow! Thank you!
Donate Us With
