Random Forest.md

Random Forest further modelling - Breast Cancer Survival Prediction using R

1. Random Forest – best ntree

load the package

library (randomForest)

read file

all_data <- read.csv (file='D:/model_evaluation/all_data.csv')

run randomForest

all_data.rf <- randomForest (V24 ~. , data=all_data, ntree=500, mtry=5, keep.forest=FALSE, importance=TRUE)

print result of randomForest (OOB error and confusion matrix)

print (all_data.rf)

find best ntree for Random Forest

which.min(all_data.rf$err.rate[,1])

2. Random Forest Further Modelling – for all clusters

Repeat the steps in (1) with different clusters of data to determine the accuracy of the model

3. Random Forest – calibration plot using Phyton 3

Import packages

import numpy as np import pandas as pd import matplotlib.pyplot as plt from sklearn import preprocessing from sklearn import model_selection

Import dataset

x_train_file = 'x_train.xlsx' y_train_file = 'y_train.xlsx' def my_data(file_name): data = np.array(pd.read_excel(file_name, 'Sheet1')) scaler=preprocessing.MinMaxScaler() data=scaler.fit_transform(data) data=data.astype('float32') return data

split the data into training and test set

bc_X_train, bc_X_test, bc_y_train, bc_y_test = train_test_split( my_data(x_train_file),my_data(y_train_file),test_size=0.2)

Import RandomForestClassifier package

from sklearn.ensemble import RandomForestClassifier rf_model = RandomForestClassifier(random_state=1234).fit(X= bc_X_train, y= bc_y_train) rf_prediction = rf_model.predict_proba(bc_X_test)

Import calibration curve package

from sklearn.calibration import calibration_curve

compute calibration curve

rf_y, rf_x = calibration_curve(bc_y_test, rf_prediction[:,1], n_bins=10)

Plot the calibration lines

import matplotlib.pyplot as plt import matplotlib.lines as mlines import matplotlib.transforms as mtransforms fig, ax = plt.subplots()

only this line is calibration curve

plt.plot(rf_x, rf_y, marker='o', linewidth=1, label='rf')

reference line, legends, and axis labels

line = mlines.Line2D([0, 1], [0, 1], color='black') transform = ax.transAxes line.set_transform(transform) ax.add_line(line) fig.suptitle('Calibration plot for Breast Cancer data') ax.set_xlabel('Predicted survival') ax.set_ylabel('True probability in each bin') plt.legend() plt.show()