#### t-distributed stochastic neighbor embedding (t-SNE) is a statistical method for visualizing high-dimensional data by giving each datapoint a location in a two dimensional map. In the plot above we used perplexity of 40 for visualizing the t-SNE, but we didnt do any dimensionality reduction with it. Blue points are Cancer and the red are non-cancer.
### The ROC curve above shows that the XGBoost model has an AUC of 0.97 which indicates that the model preformed good in classification of non cancer and cancer.
plt.title('XGBoost - ROC Curve for Training Data')
plt.legend(loc='lower right')
plt.show()
```
%% Output
%% Cell type:markdown id:942e1a1f tags:
#### In the ROC curve above we can see that the AUC value of 1 for the train set which indicates the model's flawless ability to distinguish between positive and negative classes during training, showcasing excellent performance on the training data.
### The model displayed high precision and recall for both classes, resulting in an overall F1-score of 0.93, indicating its strong performance on the test dataset.
### The XGBoost model achieved 26 true positive predictions and 30 true negative predictions, with only 4 false positives and no false negatives, indicating strong overall performance in correctly classifying positive and negative samples.
### The XGBoost model demonstrated strong performance with an accuracy of 93.3%, correctly identifying all negative samples with a specificity of 100% and achieving a high sensitivity of 86.7% for positive samples.