rasbt

20 ms·

181.
by rasbt 12y ago
Sounds cool. I think the most crucial part is really the data (or feature) set. The choice of classifiers makes a difference too, but having good data (salient, invariant, discriminatory) makes the largest difference. One think about naive
182.
by rasbt 12y ago
I like this simple and intuitive explanation. Hm, maybe a short mentioning of the "a priori" knowledge would be also worthwhile!
183.
by rasbt 12y ago
Thanks a lot, I always called it "smoothening" for whatever reason, but it should be fixed now :)
184.
Naive Bayes and Text Classification I – Introduction and Theory(sebastianraschka.com)
115 points by rasbt 12y ago|24 comments
185.
An Overview of General Performance Metrics of Binary Classifier Systems [pdf](sebastianraschka.com)
5 points by rasbt 12y ago|0 comments
186.
Kernel tricks and nonlinear dimensionality reduction via RBF kernel PCA(sebastianraschka.com)
3 points by rasbt 12y ago|0 comments
187.
Monte Carlo Approximation of Pi via a Mossberg 500 pump-action shotgun [pdf](arxiv.org)
1 points by rasbt 12y ago|1 comments
188.
About predictive modeling and supervised machine learning(sebastianraschka.com)
3 points by rasbt 12y ago|0 comments
189.
Supervised machine learning – a flow chart(raw.githubusercontent.com)
1 points by rasbt 12y ago|0 comments
190.
Streamline your cross-validation workflow – scikit-learn's Pipeline in action(nbviewer.ipython.org)
1 points by rasbt 12y ago|0 comments
191.
by rasbt 12y ago
Yes, I see Linear Discriminant Analysis more as useful tool for pre-processing and dimensionality reduction rather than using it as classifier
192.
Linear Discriminant Analysis bit by bit(sebastianraschka.com)
17 points by rasbt 12y ago|3 comments
193.
A collection of Python's most useful data science libraries(github.com)
2 points by rasbt 12y ago|0 comments
194.
by rasbt 12y ago
It's an interesting article with some interesting thoughts. However, unit tests are - for me - a crucial framework and also a main pillar in my project planning process: If unit tests are written before the actual code, it is immensely
195.
Why Most Unit Testing is Waste(pythontesting.net)
9 points by rasbt 12y ago|1 comments
196.
Examples to prepare your matplotlib plots for publications(nbviewer.ipython.org)
2 points by rasbt 12y ago|0 comments
197.
A quick guide for dealing with missing values in NumPy(nbviewer.ipython.org)
2 points by rasbt 12y ago|0 comments
198.
Estimating free energies of binding and AutoDock's semi-empirical force field(sebastianraschka.com)
2 points by rasbt 12y ago|0 comments
199.
10 types of regressions. Which one to use?(reddit.com)
1 points by rasbt 12y ago|0 comments
200.
Data Science definitions in less than 50 words(github.com)
2 points by rasbt 12y ago|0 comments
201.
A questionable practice: Dixon's Q test for outlier identification(sebastianraschka.com)
1 points by rasbt 12y ago|0 comments
202.
A matrix cheat sheet for Matlab, Python NumPy, R, and Julia(sebastianraschka.com)
2 points by rasbt 12y ago|0 comments
203.
Watermark extension for IPython: a step towards reproducible results(nbviewer.ipython.org)
2 points by rasbt 12y ago|0 comments
204.
Apple's Swift climbs quickly in language popularity(infoworld.com)
1 points by rasbt 12y ago|0 comments
205.
Social Engagement in Mobile Applications(thebhwgroup.com)
1 points by rasbt 12y ago|0 comments
206.
A collection of Algorithms in IPython notebooks(github.com)
3 points by rasbt 12y ago|0 comments
207.
A matplotlib gallery in IPython notebooks(github.com)
1 points by rasbt 12y ago|0 comments
208.
Kernel density estimation via the Parzen–Rosenblatt window method(sebastianraschka.com)
1 points by rasbt 12y ago|0 comments
209.
Algorithm identifies rare genetic disorders from family pics(wired.co.uk)
3 points by rasbt 12y ago|0 comments
210.
by rasbt 12y ago
I recently started to use pandas a lot for my data analyses. However, I didn't get to write about it yet. But it will surely follow one day :)

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