Agile-data-science-improving-prediction-performance
提供：Dev Guides
予測パフォーマンスの改善
この章では、多くの属性が含まれる生徒の成績の予測に役立つモデルの構築に焦点を当てます。 焦点は、試験で生徒の不合格結果を表示することです。
プロセス
評価の目標値はG3です。 この値はビニングされ、失敗と成功としてさらに分類されます。 G3値が10以上の場合、学生は試験に合格します。
例
学生の場合、パフォーマンスを予測するためにコードが実行される次の例を考えてください-
import pandas as pd
""" Read data file as DataFrame """
df = pd.read_csv("student-mat.csv", sep=";")
""" Import ML helpers """
from sklearn.preprocessing import LabelEncoder
from sklearn.model_selection import train_test_split
from sklearn.metrics import confusion_matrix
from sklearn.model_selection import GridSearchCV, cross_val_score
from sklearn.pipeline import Pipeline
from sklearn.feature_selection import SelectKBest, chi2
from sklearn.svm import LinearSVC # Support Vector Machine Classifier model
""" Split Data into Training and Testing Sets """
def split_data(X, Y):
return train_test_split(X, Y, test_size=0.2, random_state=17)
""" Confusion Matrix """
def confuse(y_true, y_pred):
cm = confusion_matrix(y_true=y_true, y_pred=y_pred)
# print("\nConfusion Matrix: \n", cm)
fpr(cm)
ffr(cm)
""" False Pass Rate """
def fpr(confusion_matrix):
fp = confusion_matrix[0][1]
tf = confusion_matrix[0][0]
rate = float(fp)/(fp + tf)
print("False Pass Rate: ", rate)
""" False Fail Rate """
def ffr(confusion_matrix):
ff = confusion_matrix[1][0]
tp = confusion_matrix[1][1]
rate = float(ff)/(ff + tp)
print("False Fail Rate: ", rate)
return rate
""" Train Model and Print Score """
def train_and_score(X, y):
X_train, X_test, y_train, y_test = split_data(X, y)
clf = Pipeline([
('reduce_dim', SelectKBest(chi2, k=2)),
('train', LinearSVC(C=100))
])
scores = cross_val_score(clf, X_train, y_train, cv=5, n_jobs=2)
print("Mean Model Accuracy:", np.array(scores).mean())
clf.fit(X_train, y_train)
confuse(y_test, clf.predict(X_test))
print()
""" Main Program """
def main():
print("\nStudent Performance Prediction")
# For each feature, encode to categorical values
class_le = LabelEncoder()
for column in df[[school", "sex", "address", "famsize", "Pstatus", "Mjob",
"Fjob", "reason", "guardian", "schoolsup", "famsup", "paid", "activities",
"nursery", "higher", "internet", "romantic"]].columns:
df[column] = class_le.fit_transform(df[column].values)
# Encode G1, G2, G3 as pass or fail binary values
for i, row in df.iterrows():
if row["G1"] >= 10:
df["G1"][i] = 1
else:
df["G1"][i] = 0
if row["G2"] >= 10:
df["G2"][i] = 1
else:
df["G2"][i] = 0
if row["G3"] >= 10:
df["G3"][i] = 1
else:
df["G3"][i] = 0
# Target values are G3
y = df.pop("G3")
# Feature set is remaining features
X = df
print("\n\nModel Accuracy Knowing G1 & G2 Scores")
print("=====================================")
train_and_score(X, y)
# Remove grade report 2
X.drop(["G2"], axis = 1, inplace=True)
print("\n\nModel Accuracy Knowing Only G1 Score")
print("=====================================")
train_and_score(X, y)
# Remove grade report 1
X.drop(["G1"], axis=1, inplace=True)
print("\n\nModel Accuracy Without Knowing Scores")
print("=====================================")
train_and_score(X, y)
main()出力
上記のコードは、次のように出力を生成します
予測は、1つの変数のみを参照して処理されます。 1つの変数を参照すると、生徒のパフォーマンス予測は以下のようになります-
