XGBoost現(xiàn)在風頭正盛，把它也用在Titanic試試咯
這個Kernel值得一試

import pandas as pd
import xgboost as xgb
from sklearn.preprocessing import LabelEncoder
import numpy as np

# Load the data
train_df = pd.read_csv(r'C:\Users\oncem\Desktop\Titanic\train.csv', header=0)
test_df = pd.read_csv(r'C:\Users\oncem\Desktop\Titanic\test.csv', header=0)

# We'll impute missing values using the median for numeric columns and the most
# common value for string columns.
# This is based on some nice code by 'sveitser' at http://stackoverflow.com/a/25562948
from sklearn.base import TransformerMixin
class DataFrameImputer(TransformerMixin):
    def fit(self, X, y=None):
        self.fill = pd.Series([X[c].value_counts().index[0]
            if X[c].dtype == np.dtype('O') else X[c].median() for c in X],
            index=X.columns)
        return self
    def transform(self, X, y=None):
        return X.fillna(self.fill)

feature_columns_to_use = ['Pclass','Sex','Age','Fare','Parch']
nonnumeric_columns = ['Sex']

# Join the features from train and test together before imputing missing values,
# in case their distribution is slightly different
big_X = train_df[feature_columns_to_use].append(test_df[feature_columns_to_use])
big_X_imputed = DataFrameImputer().fit_transform(big_X)

# XGBoost doesn't (yet) handle categorical features automatically, so we need to change
# them to columns of integer values.
# See http://scikit-learn.org/stable/modules/preprocessing.html#preprocessing for more
# details and options
le = LabelEncoder()
for feature in nonnumeric_columns:
    big_X_imputed[feature] = le.fit_transform(big_X_imputed[feature])

# Prepare the inputs for the model
train_X = big_X_imputed[0:train_df.shape[0]].as_matrix()
test_X = big_X_imputed[train_df.shape[0]::].as_matrix()
train_y = train_df['Survived']

# You can experiment with many other options here, using the same .fit() and .predict()
# methods; see http://scikit-learn.org
# This example uses the current build of XGBoost, from https://github.com/dmlc/xgboost
gbm = xgb.XGBClassifier(max_depth=3, n_estimators=300, learning_rate=0.05).fit(train_X, train_y)
predictions = gbm.predict(test_X)

# Kaggle needs the submission to have a certain format;
# see https://www.kaggle.com/c/titanic-gettingStarted/download/gendermodel.csv
# for an example of what it's supposed to look like.
submission = pd.DataFrame({ 'PassengerId': test_df['PassengerId'],
                            'Survived': predictions })
submission.to_csv("submission.csv", index=False)

最終測試結果0.886644219978蔑舞，比RF好较性，最重要的是快啊