ML-Exoplanet-Exploration
Preprocess the Data:
- Preprocess the dataset prior to fitting the model.
- Perform feature selection and remove unnecessary features.
- Use MinMaxScaler to scale the numerical data.
- Separate the data into training and testing data.
Tune Model Parameters:
- Use GridSearch to tune model parameters.
- Tune and compare three different classifiers (Support Vector Machine, Random Forest Classifier & Neural Network).
Analysis:
- Random forests are simpler to train; it’s easier to find a good model. The complexity of a random forest grows with the number of trees in the forest, and the number of training samples we have. In SVMs, we need to do a fair amount of parameter tuning.
Random Forest:
| Before | After | |
|---|---|---|
| Training Score | 0.995 | 1.0 |
| Testing Score | 0.883 | 0.899 |
Support Vector Machine:
| Before | After | |
|---|---|---|
| Training Score | 0.837 | 0.871 |
| Testing Score | 0.855 | 0.883 |
- Although the Random Forest model was quite successful, it would be important to train and test additional models for accuracy and reliability.
Neural Network:
- The analysis showed that Neural Network yielded an accuracy of 0.88 precision and recall of the algorithms tested.