Early stopping for XGBoost + Update Readme

[inventormc]

This PR supports early stopping for XGBoost. We leverage the incremental learning capabilities of XGBoost:

Note that this may not necessarily improve performance but instead allows us to break the training process into multiple parts.

clf = XGBClassifier(n_estimators=10, nthread=8)
base_model = None
for i in range(20):
    z = clf.fit(x_tr, y_tr, xgb_model=base_model)
    y_te = z.predict(x_te)
    print(sklearn.metrics.mean_squared_error(y_te, y_pr))
    base_model = z.get_booster()

resolves #58

[inventormc]

microsoft/LightGBM#3057 -init_model doesn't exist in the latest stable release yet