Machine learning model was created by reading an Excel file where data was stored. I applied RandomForestRegressor to create a model that predicts the size of the sieve particles according to pressure, but the value of R2 is too large negative. I found out through googling that R2 can be negative, but I don't know what it means to have such a large negative. When I applied the same amount of different data to the designed model, R2 showed a result that was close to 1, but I don't know why this data is only large negative. RMSE and SCORE scores are good, but I don't understand if only R2 scores are bad... I would appreciate it if you could let me know what is the problem and what to consider.
import pandas as pd import numpy as np import sklearn from sklearn.model_selection import train_test_split from sklearn.ensemble import RandomForestRegressor from sklearn.ensemble import RandomForestClassifier from sklearn import metrics from google.colab import drive from sklearn.metrics import mean_squared_error from sklearn.metrics import r2_score drive.mount('/gdrive', force_remount=True) data = pd.read_csv(r"/gdrive/MyDrive/Coal_Inert_Case/Inert_Case_1.csv") x =data[['press1', 'press2', 'press3', 'press4', 'press5', 'press6', 'press7', 'press8', 'press9', 'press10', 'press11', 'press12', 'press13', 'press14', 'press15', 'press16', 'press17', 'press18', 'press19', 'press20', 'press21', 'press22', 'press23', 'press24', 'press25', 'press26', 'press27', 'press28', 'press29', 'press30', 'press31', 'press32', 'press33', 'press34', 'press35', 'press36', 'press37', 'press38', 'press39', 'press40', 'press41', 'press42', 'press43', 'press44', 'press45', 'press46', 'press47', 'press48', 'press49', 'press50', 'press51', 'press52', 'press53']] y = data[['Sieve 16000', 'Sieve 11000', 'Sieve 8000', 'Sieve 5600', 'Sieve 4000', 'Sieve 2800', 'Sieve 2000', 'Sieve 1400', 'Sieve 1000', 'Sieve 710', 'Sieve 500', 'Sieve 355', 'Sieve 250', 'Sieve 180', 'Sieve 125', 'Sieve 90', 'Sieve 63', 'Sieve 44', 'Sieve 31', 'Sieve 0']] X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.3,random_state= 42) forest = RandomForestRegressor(n_estimators=1000,random_state= 42) forest.fit(X_train, y_train) y_pred = forest.predict(X_test) mse = mean_squared_error(y_test, y_pred) rmse = np.sqrt(mse) r2_y_predict = r2_score(y_test, y_pred) print("RMSE:", rmse) print("R2 : ", r2_y_predict)
The output (RMSE, R2) is:
RMSE: 0.6913667737213217 R2 : -7294765918428.414