> For the complete documentation index, see [llms.txt](https://polinema.gitbook.io/jti-modul-praktikum-pembelajaran-mesin/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://polinema.gitbook.io/jti-modul-praktikum-pembelajaran-mesin/ensemble-learning/job-sheet-6-ensemble-learning/praktikum-1.md). # Praktikum 1 **Download Dataset Terlebih Dahulu** :arrow\_down: {% file src="/files/Key6k1HstAZYYk63gLo9" %} ### Bagging dengan RandomForest Pada kasus ini kita akan menggunakan salah satu metode bagging yaitu RandomForest untuk mengklasifikasikan jenis tumor. Dalam latihan ini Anda akan melakukan training dengan data [Wisconsin Breast Cancer Dataset](https://www.kaggle.com/uciml/breast-cancer-wisconsin-data) dari UCI machine learning repository. Latihan ini akan melakukan prediksi memprediksi apakah tumor ganas atau jinak. Kita akan membandingkan performa dari algoritma **Decision Tree** dan **RandomForest** pada kasus ini. #### Import Library ```python import numpy as np import pandas as pd from sklearn.tree import DecisionTreeClassifier # import DT from sklearn.ensemble import RandomForestClassifier # import RandomForest from sklearn.model_selection import train_test_split from sklearn.metrics import accuracy_score, classification_report ``` #### Persiapan Data ```python # Load data df = pd.read_csv('data/wbc.csv') df.head() ``` ``` id diagnosis radius_mean texture_mean perimeter_mean area_mean \ 0 842302 M 17.99 10.38 122.80 1001.0 1 842517 M 20.57 17.77 132.90 1326.0 2 84300903 M 19.69 21.25 130.00 1203.0 3 84348301 M 11.42 20.38 77.58 386.1 4 84358402 M 20.29 14.34 135.10 1297.0 smoothness_mean compactness_mean concavity_mean concave points_mean \ 0 0.11840 0.27760 0.3001 0.14710 1 0.08474 0.07864 0.0869 0.07017 2 0.10960 0.15990 0.1974 0.12790 3 0.14250 0.28390 0.2414 0.10520 4 0.10030 0.13280 0.1980 0.10430 ... texture_worst perimeter_worst area_worst smoothness_worst \ 0 ... 17.33 184.60 2019.0 0.1622 1 ... 23.41 158.80 1956.0 0.1238 2 ... 25.53 152.50 1709.0 0.1444 3 ... 26.50 98.87 567.7 0.2098 4 ... 16.67 152.20 1575.0 0.1374 compactness_worst concavity_worst concave points_worst symmetry_worst \ 0 0.6656 0.7119 0.2654 0.4601 1 0.1866 0.2416 0.1860 0.2750 2 0.4245 0.4504 0.2430 0.3613 3 0.8663 0.6869 0.2575 0.6638 4 0.2050 0.4000 0.1625 0.2364 fractal_dimension_worst Unnamed: 32 0 0.11890 NaN 1 0.08902 NaN 2 0.08758 NaN 3 0.17300 NaN 4 0.07678 NaN [5 rows x 33 columns] ``` ```python # Cek kolom null df.isnull().sum() ``` ``` id 0 diagnosis 0 radius_mean 0 texture_mean 0 perimeter_mean 0 area_mean 0 smoothness_mean 0 compactness_mean 0 concavity_mean 0 concave points_mean 0 symmetry_mean 0 fractal_dimension_mean 0 radius_se 0 texture_se 0 perimeter_se 0 area_se 0 smoothness_se 0 compactness_se 0 concavity_se 0 concave points_se 0 symmetry_se 0 fractal_dimension_se 0 radius_worst 0 texture_worst 0 perimeter_worst 0 area_worst 0 smoothness_worst 0 compactness_worst 0 concavity_worst 0 concave points_worst 0 symmetry_worst 0 fractal_dimension_worst 0 Unnamed: 32 569 dtype: int64 ``` ```python # Seleksi fitur # Slice dataframe mulai dari kolom 'radius_mean' sampai 'fractal_dimension_worst' X = df.iloc[:,3:-1] y = df['diagnosis'] y = y.map({'M':1, 'B':0}) # Encode label # Cek jumlah fitur dan instance X.shape ``` ``` (569, 30) ``` #### Split data training dan testing ```python from sklearn.model_selection import train_test_split X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1) ``` #### Traning Decision Tree ```python # Secara default, DecisionTreeClassifier dari scikit-learn akan menggunakan nilai "Gini" untuk kriteria # Terdapat beberapa "hyperparamater" yang dapat digunakan. Silahka baca dokumentasi # Pada kasus ini kita akan menggunakan parameter default dt = DecisionTreeClassifier() # Sesuaikan dt ke set training dt.fit(X_train, y_train) # Memprediksi label set test y_pred_dt = dt.predict(X_test) # menghitung set accuracy acc_dt = accuracy_score(y_test, y_pred_dt) print("Test set accuracy: {:.2f}".format(acc_dt)) print(f"Test set accuracy: {acc_dt}") ``` ``` Test set accuracy: 0.95 Test set accuracy: 0.9473684210526315 ``` #### Training RandomForest ```python # Pada kasus kali ini kita akan menggunakan estimator pada RandomForest # Untuk detail parameter (hyperparameter) silahkan cek dokumentasi rf = RandomForestClassifier(n_estimators=10, random_state=1) # Sesuaikan dt ke set training rf.fit(X_train, y_train) # Memprediksi label set test y_pred_rf = rf.predict(X_test) # menghitung set accuracy acc_rf = accuracy_score(y_test, y_pred_rf) print("Test set accuracy: {:.2f}".format(acc_rf)) print(f"Test set accuracy: {acc_rf}") ``` ``` Test set accuracy: 0.96 Test set accuracy: 0.956140350877193 ``` --- # Agent Instructions This documentation is published with GitBook. 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