kaggle Titanic Tutorial – 9

さて今回は少し趣向を変えて別のアルゴリズムを試してみる。

アルゴリズムの試し方はこちらを参考にした。

https://www.kaggle.com/omarelgabry/a-journey-through-titanic?scriptVersionId=447794

関連するコードは以下の通り。

Y_train = d_train["Survived"].values
X_train = d_train.drop("Survived",axis=1)
X_test = d_test.drop("PassengerId",axis=1).copy()

from sklearn.tree import DecisionTreeClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC, LinearSVC
from sklearn.ensemble import RandomForestClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.naive_bayes import GaussianNB
dtree = DecisionTreeClassifier(max_depth=8)
dtree.fit(x_train,y_train)
Y_pred = dtree.predict(X_test)
dtree.score(X_train, Y_train)

logreg = LogisticRegression()
logreg.fit(X_train, Y_train)
Y_pred = logreg.predict(X_test)
logreg.score(X_train, Y_train)

svc = SVC()
svc.fit(X_train, Y_train)
Y_pred = svc.predict(X_test)
svc.score(X_train, Y_train)

random_forest = RandomForestClassifier(n_estimators=100)
random_forest.fit(X_train, Y_train)
Y_pred = random_forest.predict(X_test)
random_forest.score(X_train, Y_train)

knn = KNeighborsClassifier(n_neighbors = 3)
knn.fit(X_train, Y_train)
Y_pred = knn.predict(X_test)
knn.score(X_train, Y_train)

gaussian = GaussianNB()
gaussian.fit(X_train, Y_train)
Y_pred = gaussian.predict(X_test)
gaussian.score(X_train, Y_train)

この結果として

DecisionTree : 0.89113355780022452

Logistic Regression : 0.80246913580246915

SVC : 0.90460157126823793

KNN : 0.84062850729517391

GaussianNB : 0.80808080808080807

とりあえず、SVCでサブミッションしてみる。

いまいちでした。