[Side Project After Work] Big Data Analysis Certification Practical Exam (Type 1, 2, 3)
We guide non-majors and beginners to quickly obtain the Big Data Analysis Certification (Practical Exam)! Keep the theory light and the practice solid—focusing on core points that are guaranteed to appear on the exam through past questions, without the need for complex background knowledge.
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5,286 learners
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Engineer Big Data Analysis
Engineer Big Data Analysis
Big Data
Big Data
Python
Python
Pandas
Pandas
Machine Learning(ML)
Machine Learning(ML)
Engineer Big Data Analysis
Engineer Big Data Analysis
Big Data
Big Data
Python
Python
Pandas
Pandas
Machine Learning(ML)
Machine Learning(ML)
3 ways to calculate regression evaluation index RMSE
Here are three ways to calculate the regression evaluation metric RMSE.
You don't necessarily have to create a function.
After finding mse, square it or
Please use the squared=False setting 💪
from sklearn.metrics import mean_squared_error import numpy as np # 예제 데이터 y_true = np.array([3, -0.5, 2, 7]) y_pred = np.array([2.5, 0.0, 2, 8]) # MSE 계산 mse = mean_squared_error(y_true, y_pred) print(f'MSE: {mse}') # RMSE 방법1 (squared=False) rmse = mean_squared_error(y_true, y_pred, squared=False) print(f'RMSE: {rmse}') # RMSE 방법2 rmse = np.sqrt(mse) print(f'RMSE(np): {rmse}') # RMSE 방법3 rmse = mse ** 0.5 print(f'RMSE ** 0.5: {rmse}')Comment