kaggle-Titanic | python 2 | Machine Learning library

I found the problem, The problem was that the sparkxbg.zip(which I downloaded over internet) is written for xgboost4j-0.72. However, my jars were from xgoost4j-0.9. And the API has been completetly changed. As a result 0.9 version didn't had any class named ml.dmlc.xgboost4j.scala.spark.XGBoostEstimator. And hence the error. You can see the difference in API below:

https://github.com/dmlc/xgboost/tree/release_0.72/jvm-packages/xgboost4j-spark/src/main/scala/ml/dmlc/xgboost4j/scala/spark

vs

https://github.com/dmlc/xgboost/tree/v0.90/jvm-packages/xgboost4j-spark/src/main/scala/ml/dmlc/xgboost4j/scala/spark

The error is probably because you are trying to iterate from your train DataFrame instead of the list of DataFrames train_test_data.

Try simply doing:

For such issues, arguably your best friend is the documentation; quoting from scikit-learn docs on model evaluation:

There are 3 different APIs for evaluating the quality of a model’s predictions:

• Estimator score method: Estimators have a score method providing a default evaluation criterion for the problem they are designed to solve. This is not discussed on this page, but in each estimator’s documentation.
• Scoring parameter: Model-evaluation tools using cross-validation (such as model_selection.cross_val_score and model_selection.GridSearchCV) rely on an internal scoring strategy. This is discussed in the section The scoring parameter: defining model evaluation rules.
• Metric functions: The metrics module implements functions assessing prediction error for specific purposes. These metrics are detailed in sections on Classification metrics, Multilabel ranking metrics, Regression metrics and Clustering metrics.

In the docs of all 3 classifiers you are using in your code (logistic regression, random forest, and decision tree, there is the identical description:

score(X, y, sample_weight=None)
Returns the mean accuracy on the given test data and labels.

which answers your 2nd question for the specific models used.

Nevertheless, you should always check the docs before blindly trusting the score method coming with an estimator; in linear regression and desision tree regressor, for example, score returns the coefficient of determination R^2, which is practically never used by ML practitioners building predictive models (it is often used by statisticians building explanatory models, but that's another story).

BTW, I glimpsed briefly at the code you link to, and I saw that you compute metrics like MSE, MAE, and RMSE - keep in mind that these are regression metrics, and they are not meaningful in a classification setting, such as the one you face here (and in turn, accuracy is meaningless in regression settings)...

Difference is str.contains also match Mrs., because . is special regex character (it is used to match any character).

I think need escape it or add parameter regex=False: