2017-01-20
Created: January 20, 2017 / Updated: March 22, 2020 / Status: in progress / 2 min read (~389 words)
- Using jupyter for data exploration
- Use various tools from the sklearn library
- It's useful to have a small utility library that will copy your train.py script such that you have an history of what your script was like at every run. This way if you need to backtrack to your "best" results, you can look at your tensorflow results, find the timestamp of the execution, and then find the related train.py script.
- I could not use sklearn right away due to the fact that my numpy library is 1.12.0b1+mkl. I had to edit sklearn/utils/fixes.py, line 406 from
if np_version < (1, 12, 0):toif np_version < (1, 12):. - Plots were very small initially.
- Pretty present matlibplot plots: when using a plot with multiple subplots, the axis labels often overlap with one another. I tried to use
tight_layoutbut was unable to use it to display the data I wanted it to use. - In the tutorial I followed, the author uses
dataset.plot(kind="box",...)which supposedly only renders out the 4 inputs, while in my case it was also rendering the output. I was never able to only render out only my inputs without having to change the dataset variable itself.
Since I was able to make much progress in the last few days, I decided to take a different approach. I am far from an expert, so I decided I would follow some newbie tutorial on machine learning, but apply it to my data.
I followed the introductory tutorial available @ http://machinelearningmastery.com/machine-learning-in-python-step-by-step/. It allowed me to learn a few neat tricks that makes it possible for me to rapidly inspect the data (head/tail/describe/plot(kind="box")/hist on a dataset, groupby(...).size() to get an idea of classes distribution).
Through the tutorial, I was lead to try various models: logistic regression, linear discriminant analysis, k neighbors classifier, decision tree classifier, Gaussian naïve Bayes and support vector clustering.
Model: Cross-validation mean (std deviation)
LR: 0.466854 (0.006114)
LDA: 0.472471 (0.005050)
KNN: 0.568854 (0.005844)
CART: 0.794611 (0.005026)
NB: 0.165576 (0.005248)
SVM: No result (did not complete)
I was glad to see that the decision tree classifier was able to get 79%, compared to the ~40% I could get at best with my dense NN.