How will you select the best algorithm for your problem?

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How will you select the best algorithm for your problem?

Here are some important considerations while choosing an algorithm.

  • Size of the training data. It is usually recommended to gather a good amount of data to get reliable predictions.
  • Accuracy and/or Interpretability of the output.
  • Speed or Training time.
  • Linearity.
  • Number of features.

Which algorithm is best for data analysis?

The most popular Machine Learning algorithms used by the Data Scientists are:

  1. Linear Regression.
  2. Logistic Regression.
  3. Decision Trees.
  4. Naive Bayes.
  5. KNN.
  6. Support Vector Machine (SVM)
  7. K-Means Clustering.
  8. Principal Component Analysis (PCA)

Which is better Overfitting or Underfitting?

Overfitting: Good performance on the training data, poor generliazation to other data. Underfitting: Poor performance on the training data and poor generalization to other data.

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What are the best algorithms for learning binary classification?

It is important to have a good mix of algorithm representations (lines, trees, instances, etc.) as well as algorithms for learning those representations. A good rule of thumb I use is “a few of each”, for example in the case of binary classification: Linear methods: Linear Discriminant Analysis and Logistic Regression.

What is the best machine learning algorithm for beginners?

The Top 10 Machine Learning Algorithms Every Beginner Should Know. 1 1 — Linear Regression. Linear regression is perhaps one of the most well-known and well-understood algorithms in statistics and machine learning. 2 2 — Logistic Regression. 3 3 — Linear Discriminant Analysis. 4 4 — Classification and Regression Trees. 5 5 — Naive Bayes.

Is it possible to know the best algorithm for a problem?

If you or anyone knew what algorithm gave the best results for a specific dataset, then you probably would not need to use machine learning in the first place because of your deep knowledge of the problem. We cannot know beforehand the best algorithm representation or learning algorithm for that representation to use.

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What is the accuracy of the class classification algorithm?

Classification Algorithms Accuracy F1-Score Logistic Regression 84.60\% 0.6337 Naïve Bayes 80.11\% 0.6005 Stochastic Gradient Descent 82.20\% 0.5780 K-Nearest Neighbours 83.56\% 0.5924