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Scikit-Learn : เปรียบเทียบประสิทธิภาพการทำงานกับข้อมูล IRIS ของ Classification Algorithms แบบต่างๆ

By admin | Oct. 4, 2019, 1:11 p.m. | comments 331

ก่อนจะศึกษารายละเอียดต่างๆ ในบทความนี้ หากท่านยังไม่มีความรู้เบื้องต้น หรือยังไม่เคยศึกษาเกี่ยวกับ machine learning มาก่อน ขอแนะนำให้เริ่มต้นจาก เริ่มต้นศึกษา Machine Learning ด้วย Python และ Scikit-Learn. แล้วจึงกลับมาศึกษาในรายละเอียดของบทความนี้

การตรวจสอบประสิทธิภาพของการทำงาน ของ algorithms แบบต่างๆ เป็นแนวทางเพื่อการเลือกใช้ algorithms ที่มีประสิทธิภาพการทำงานสูงสุด

เครื่องมือ

python (3.x.x)
Scikit-Learn

(หากยังไม่มีการติดตั้งในระบบ)

download python และติดตั้ง
เมื่อติดตั้ง python แล้ว ทำการ install SciKit-Learn โดย run Command prompt ของ windows และติดตั้งโดยคำสั่ง pip ดังนี้

C:\.....\.....\>pip install scikit-learn

เริ่มเขียนโปรแกรม

เรียกใช้งาน python idle
สร้างแฟ้มใหม่
เขียนโปรแกรม
รันโปรแกรม และ ตรวจสอบผลลัพธ์

โปรแกรม

from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
from sklearn.tree import DecisionTreeClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.svm import SVC
from sklearn.naive_bayes import GaussianNB
from sklearn.linear_model import LogisticRegression

iris_data = load_iris()

x=iris_data.data
y=iris_data.target

x_train,x_test,y_train,y_test = train_test_split(x,y, random_state=0)

models = []
models.append(('DTC', DecisionTreeClassifier()))
models.append(('KNN', KNeighborsClassifier()))
models.append(('LDA', LinearDiscriminantAnalysis()))
models.append(('SVM', SVC(gamma='auto')))
models.append(('GNB', GaussianNB()))
models.append(('LTR', LogisticRegression(solver='liblinear', multi_class='ovr')))

for name, model in models:
    model_fit = model.fit(x_train,y_train)
    model_predict = model_fit.predict(x_test)
    result = accuracy_score(y_test, model_predict)
    print("%s: %f" % (name, result))

ผลการทำงาน

DTC: 0.973684
KNN: 0.973684
LDA: 0.973684
SVM: 0.973684
GNB: 1.000000
LTR: 0.868421

ถาม-ตอบ

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