mljar-examples VS Python_NN

All ML experiments have automatic documentation that creates Markdown reports ready to commit to the repo (example1, example2).

The creator here. I'm working on AutoML since 2016. I think that latest release (0.7.15) of MLJAR AutoML is amazing. It has ton of fantastic features that I always want to have in AutoML:

- Operates in three modes: Explain, Perform, Compete.

- `Explain` is for data exploratory and checking the default performance (without HP tuning). It has Automatic Exploratory Data Analysis.

- `Perform` is for building production-ready models (HP tuning + ensembling).

- `Compete` is for solving ML competitions in limited time amount (HP tuning + ensembling + stacking).

- All ML experiments have automatic documentation which creates Markdown reports ready to commit to the repo ([example](https://github.com/mljar/mljar-examples/tree/master/Income_c...)).

- The package produces extensive explanations: decision tree visualization, feature importance, SHAP explanations, advanced metrics values.

- It has advanced feature engineering, like: Golden Features, Features Selection, Time and Text Transformations, Categoricals handling with target, label, or one-hot encodings.

About the issue with repeating characters, it is supposed to happen. There are certain loops that repeat. To solve this you must remove the line q1 = np.argmax(p1.cpu(), axis=1)[-1].item() and instead, do sampling based on the softmax probabilities. Check this code where I perform sampling. The vec = vec**(2) is used to decrease randomness you can see how your model works and either keep it or leave it.