Kaggle evaluation metrics used for regression problems

Posted on Sat 16 February 2019 in Posts • Tagged with machine learning, evaluation, metrics, performance • 7 min read

This post describe evaluation metrics used in Kaggle competitions where problem to solve is has regression nature. Eight different metrics are described, namely: Absolute Error (AE), Mean Absolute Error (MAE), Weighted Mean Absolute Error (WMAE), Pearson Correlation Coefficient, Spearman’s Rank Correlation, Root Mean Squared Error (RMSE), Root Mean Squared Logarithmic Error (RMSLE), Mean Columnwise Root Mean Squared Error (MCRMSE).


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How to install TensorFlow and Keras on Windows 10

Posted on Thu 17 January 2019 in Posts • Tagged with machine learning, tensorflow, howto • 2 min read

Guide on how to install TensorFlow cpu-only version - the case for machines without GPU supporting CUDA. Step-by-step procedure starting from creating conda environment till testing if TensorFlow and Keras Works.


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Darwin Approach to Traveling Salesman

Posted on Sat 12 January 2019 in Posts • Tagged with machine learning, evolutionary • 6 min read

Can evolutionary approach crash the problem that brute forcing will last far more that the age of universe? This post shows how to attack Traveling Salesman Problem using Darwin approach. I'm describing evolution model and design decisions. See the animation how the population was evolving through the epochs.


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How to organize Data Science project based on Jupyter notebook

Posted on Sat 05 January 2019 in Posts • Tagged with jupyter, python, notebook, howto, machine learning • 4 min read

Having several notebook-based projects behind you might result in mess in projects directory. Organize your Data Science project based on Jupyter notebooks in a way that one can navigate through it. Especially that "the one" will be most probably you in few months time. To achieve that: keep your projects directory clean, name the project in a descriptive way and take care of internal structure of the project.


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What's cooking

Posted on Thu 05 April 2018 in Posts • Tagged with python, jupyter, kaggle, NLP, EDA, machine learning • 13 min read

Exploratory Data Anlysis of the Kaggle's "What's cooking" competition dataset to get understanding what kind of data we are dealing with and get intuition of existing dependecies.


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