Related Issues (20)
- 7. Feature distribution analysis: 1 other insight to surprise us
- 8. Conclusion: State how balanced/imbalanced the data set is in terms of Star/OD (difficulty). Summarize uneveness of other features
- 6.1 Build a function that accepts a sigmoid-like set of points, and outputs a closed function that approximates the curve
- 6.2 Evaluate the error of the function. What % of curves was it able to properly estimate? (Ex. If you're using scipy.optimize_fit, it may not converge)
- 7.2 Fit curves with two other algebraic sigmoid functions (tanh, generalised logistic, etc.)
- 7.1 Fit curves with logistic function
- 7.3 For each beatmap, convert the 3 CDFs fitted to PDFs. Visualize how beatmap features are correlated to the mean and std of the PDF
- 7.2 Fit curves with two other cummulative distribution functions (logistic, t, etc.)
- 7.1 Fit curves with normal CDF
- 9.6 Cross Validation
- 9.5 Visualization
- 9.4 Change some parameters and explore
- 9.3 Fit the model
- 9.2 Split data to training and testing
- 9.1 Import data
- Task 4: Validation
- Task 3: Training
- Task 2: Visualization
- Task 1: Get pandas dataset and make it nice.
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