Pelatihan model

Machine Learning Ujung ke Ujung

Joshua Stapleton

Machine Learning Engineer

Pisau Cukur Occam

Penjelasan memadai paling sederhana adalah yang terbaik
Utamakan model sederhana saat memilih

Contoh gambar yang menunjukkan prinsip Pisau Cukur Occam

Opsi pemodelan

Regresi Logistik

Menemukan batas keputusan antar kelas
sklearn.linear_model.LogisticRegression

Support Vector Classifier

Menemukan bidang pemisah kelas
sklearn.svm.SVC

Decision Tree

Menemukan aturan sederhana untuk mengklasifikasi data
sklearn.tree.DecisionTreeClassifier

Random Forest

Menggabungkan banyak decision tree
sklearn.ensemble.RandomForestClassifier

Model lain

Model deep learning

Neural Network
Convolutional Neural Network
Generative Pretrained Transformer (GPT)

K-Nearest Neighbors (KNN)

Algoritma pembelajaran terawasi

XGBoost

Model gradient boosting
https://xgboost.readthedocs.io/en/stable/

Prinsip pelatihan

Model:

Menggunakan data yang sudah dibersihkan dan diolah fiturnya
Mempelajari pola pada data latih
Bertujuan memprediksi target diagnosis penyakit jantung

Prinsip:

Model harus menggeneralisasi ke data baru (di luar data latih)
Sisihkan sebagian data untuk menguji model setelah pelatihan selesai
Umumnya bagi latih/uji 70/30 atau 80/20
Dapat menggunakan sklearn.model_selection.train_test_split

Melatih model

# Importing necessary libraries
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression


# Split the data into training and testing sets (80:20)
X_train, X_test, y_train, y_test = train_test_split(features, heart_disease_y, 
    test_size=0.2, random_state=42)


# Define the models
logistic_model = LogisticRegression(max_iter=200)


# Train the model
logistic_model.fit(X_train, y_train)

Mendapatkan prediksi model

# Jane Doe's health data, for example: [age, cholesterol level, blood pressure, etc.]
jane_doe_data = [45, 230, 120, ...]


# Reshape the data to 2D, because scikit-learn expects a 2D array-like input
jane_doe_data = jane_doe_data.reshape(1, -1)


# Use the model to predict Jane's heart disease diagnosis probabilities
jane_doe_probabilities = logistic_model.predict_proba(jane_doe_data)
jane_doe_prediction = logistic_model.predict(jane_doe_data)

Mendapatkan prediksi model (lanj.)

# Print the probabilities
print(f"Jane Doe's predicted probabilities: {jane_doe_probabilities[0]}")
print(f"Jane Doe's predicted health condition: {jane_doe_prediction[0]}")

Jane Doe's predicted health condition probabilities: [0.2 0.8]

Jane Doe's predicted health condition: 1

Ayo berlatih!

Machine Learning Ujung ke Ujung