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。
