How to Choose a Machine Learning Model – Some Guidelines

 

 

In this post, we explore some broad guidelines for selecting machine learning models

 

The overall steps for Machine Learning/Deep Learning are:

  • Collect data
  • Check for anomalies, missing data and clean the data
  • Perform statistical analysis and initial visualization
  • Build models
  • Check the accuracy
  • Present the results

 

Machine learning tasks can be classified into

  • Supervised learning
  • Unsupervised learning
  • Semi-supervised learning
  • Reinforcement learning

 

PS – in this document – we do not focus on the last two

 

Below are some approaches on choosing a model for Machine Learning/Deep Learning

 

OVERALL APPROACHES

 

  • Dealing with unbalanced data: Use resampling strategies            
  • Create new features : Principal component analysis (PCA) to reduce dimensionality, Autoencoders to create a latent space and possibly Clustering to create new features
  • To prevent overfitting, outliers and noise in linear regression – use regularization techniques like lasso and ridge.

 

MACHINE LEARNING MODELS

  • First approach to predicting continuous values: Linear Regression is generally a good first approach for predicting continuous values (ex: prices)
  • Binary classification: Logistic regression is a good starting point for Binary classification. Support Vector Machines SVM is also a good choice of two class classification
  • Is there a simplest or easiest model category to start off with? Decision trees are often seen as simple to understand and use. Decision trees are implemented through models such as Random forest or Gradient boosting.
  • Which models are used in Kaggle? For supervised learning: Random forest and XGboost See note on Gradient boosted trees

 

DEEP LEARNING MODELS

  • Complex features which cannot be easily specified but you have large number of labelled examples: Multi-layer perceptrons
  • Vision based Machine Learning: Image classification, Object Detection, Image segmentation – Convolutional Neural Networks
  • Sequence modelling tasks: RNNs (typically LSTM) for sequence modelling tasks ex text classification or language translation

 

Comments welcome

 

Image source: BMJ – what makes machine learning in healthcare so powerful

 

http://www.datasciencecentral.com/xn/detail/6448529:BlogPost:768280