Machine Learning is the science of teaching computers to perform certain tasks without being explicitly programmed. It can be basically divided into 3 parts-
- Supervised Learning
- Unsupervised Learning
- Reinforcement Learning
Supervised learning is the part that is majorly taught everywhere and consists of two very important tasks Classification and Regression. To know more about these tasks and a simple introduction to how to perform them using Python read my article given below.
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Basic Machine Learning Cheatsheet using Python [10 Classification & Regression Methods]
amananandrai ・ Jun 18 '20
We need to assess the performance of our machine learning models and here I am giving a list of evaluation metrics for classification and regression tasks.
Evaluation Metrics for Classification
- Accuracy
- Precision (P)
- Recall (R)
- F1 score (F1)
- Area under the ROC (Receiver Operating Characteristic) curve or simply Area Under Curve (AUC)
- Log loss
- Precision at k (P@k)
- Average precision at k (AP@k)
- Mean average precision at k (MAP@k)
Evaluation Metrics for Regression
- Mean absolute error (MAE)
- Mean squared error (MSE)
- Root mean squared error (RMSE)
- Root mean squared logarithmic error (RMSLE)
- Mean percentage error (MPE)
- Mean absolute percentage error (MAPE)
- R-square (R^2)
For a much more detailed explanation of Performance metrics you can read the article given below.
https://neptune.ai/blog/performance-metrics-in-machine-learning-complete-guide
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