How to choose Machine Learning algorithm.
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The document discusses choosing machine learning algorithms for classification problems. It recommends first visualizing the dataset using a pair plot to understand the data structure. If there is high overlap between classes, logistic regression and decision trees may not be suitable due to high error rates. For highly overlapped data, K-nearest neighbors (KNN) is recommended as it uses Euclidean distance to find similarities between data points based on their neighborhoods. Other options for highly overlapped data include random forests or deeper decision trees, but they increase computational costs. The key is to understand the dataset nature and properties before selecting an algorithm.
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How to choose Machine Learning algorithm.
- 1. Machine Learning Algorithms Dilemma of choosing WHICH !? February 23, 2020 Mala Deep Upadhaya
- 2. Machine Learning Algorithm Slides 2 of 12 Choosing Machine Learning Algorithm
- 3. Assumption in the lecture Slides 3 of 12 Choosing Machine Learning Algorithm • We have a use case: Supervised Learning system • i.e. We are concerning with ML supervised problem statement • We have Dataset that may be Classification or Regression problem set Data set Regression Classification • Now, Which Algorithm to choose for the Dataset?
- 4. Algorithm for Classification Problems Slides 4 of 12 Classification Logistic Regression KNN SVM Extreme Gradient Boosting Decision Tree Random Forest Ensemble Learning Method : Model that makes predictions based on a number of different models • Now, Which one of the above algorithm to choose for the Classification problem? Choosing Machine Learning Algorithm
- 5. Algorithm Selection Slides 5 of 12 Mostly - Blindly used • Decision Tree • Random Forest • Logistic Regression OR Apply every Algorithm parallelly and check the accuracy to see which one is the best. Task is Time & Resource Consuming Choosing Machine Learning Algorithm
- 6. Right way to choose Algorithm Visualization of Data • Library: Seaborn • Function: Pairplot Figure: Pairplot structure Source: https://seaborn.pydata.org/generated/seaborn.pairplot.html Slides 6 of 12 Choosing Machine Learning Algorithm
- 7. Right way to choose Algorithm Choose Logistic Regression? • High overlap of data • So no straight line can be created as error rate will be high • Less accuracy • Seen as non-linear classification type problem • No go with Logistic Regression Algorithm Slides 7 of 12 High Overlap of Data Choosing Machine Learning Algorithm
- 8. Right way to choose Algorithm Slides 8 of 12 • For Non-Linear classification Choosing Machine Learning Algorithm
- 9. Right way to choose Algorithm Slides 9 of 12 Choose Decision Tree? • It is just multiple IF –ELSE • Time for model train will be high in multiple overlap Choosing Machine Learning Algorithm
- 10. Right way to choose Algorithm Highly Overlap? • Choose KNN WHY? • It uses the concept of Euclidian distance to find the similarities of the point where it belongs to i.e. Based on neighborhood Still not satisfied of using KNN then go with Random Forest or Decision Tree but they will have deeper tree and increase the cost of operation Choosing Machine Learning Algorithm Highly Overlapped of Data Slides 10 of 12
- 11. Summary Slides 11 of 12Choosing Machine Learning Algorithm Dilemma of Choosing ML Algorithm? Know the nature of Dataset IF it is supervised problem statement Visualize the dataset with Pair plot Dataset is highly overlap? No use of Logical Regression No use Decision Tree as cost of operation is high Choose KNN
- 12. References • https://www.youtube.com/watch?v=38SUUaMX5Rg https://slideplayer.com/slide/5219172/ • https://towardsdatascience.com/basic-ensemble-learning-random-forest-adaboost-gradient-boosting-step-by-step-explained- 95d49d1e2725 Slides 12 of 12Choosing Machine Learning Algorithm