Create Random Forest.md

Random Forest.md

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+# Random Forest further modelling - Breast Cancer Survival Prediction
+
+## 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()