AI News, BOOK REVIEW: How to Choose Right Machine Learning Algorithms?
How to Choose Right Machine Learning Algorithms?
In this post, you will learn about tips and techniques which could be used for selecting or choosing the right machine learning algorithms for your machine learning problem.
This post deals with the following different scenarios while explaining machine learning algorithms which could be used to solve related problems: For scenarios where there are a large number of features but a lesser volume of data, one could go for some of the following machine learning algorithms: A
For scenarios where there are a smaller number of features but a large volume of data, one could go for some of the following machine learning algorithms: The examples of large data could include microarrays (gene expression data), proteomics, brain images, videos, functional data, longitudinal data, high-frequency financial data, warehouse sales, among others.
The following represents some of the techniques which could be used for processing a large number of features and associated data set while building the models: Once the aspects related to a large number of features or a large volume of data set is taken care, one could appropriately use different algorithms as described above.
Efficient Data-Driven Geologic Feature Characterization from Pre-stack Seismic Measurements using Randomized Machine-Learning Algorithm
Conventional seismic techniques for detecting the subsurface geologic features are challenged by limited data coverage, computational inefficiency, and subjective human factors.
We employ a data reduction technique in combination with the conventional kernel ridge regression method to improve the computational efficiency and reduce memory usage.
In particular, we utilize a randomized numerical linear algebra technique, named Nyström method, to effectively reduce the dimensionality of the feature space without compromising the information content required for accurate characterization.
What to do with “small” data?
Many technology companies now have teams of smart data-scientists, versed in big-data infrastructure tools and machine learning algorithms, but every now and then, a data set with very few data points turns up and none of these algorithms seem to be working properly anymore.
Most data science, relevance, and machine learning activities in technology companies have been focused around “Big Data” and scenarios with huge data sets.
And most data scientists and machine learning practitioners have gained experience is such situations, have grown accustomed to the appropriate algorithms, and gained good intuitions about the usual trade-offs (bias-variance, flexibility-stability, hand-crafted features vs.
the set of all linear models with 3 non-zero weights, the set of decision trees with depth <= 4, the set of histograms with 10 equally-spaced bins).
If you try too many different techniques, and use a hold-out set to compare between them, be aware of the statistical power of the results you are getting, and be aware that the performance you are getting on this set is not a good estimator for out of sample performance.
7- Do use Regularization Regularization is an almost-magical solution that constraints model fitting and reduces the effective degrees of freedom without reducing the actual number of parameters in the model.
L1 regularization produces models with fewer non-zero parameters, effectively performing implicit feature selection, which could be desirable for explainability of performance in production, while L2 regularization produces models with more conservative (closer to zero) parameters and is effectively similar to having strong zero-centered priors for the parameters (in the Bayesian world).
8- Do use Model Averaging Model averaging has similar effects to regularization is that it reduces variance and enhances generalization, but it is a generic technique that can be used with any type of models or even with heterogeneous sets of models.
9- Try Bayesian Modeling and Model Averaging Again, not a favorite technique of mine, but Bayesian inference may be well suited for dealing with smaller data sets, especially if you can use domain expertise to construct sensible priors.
For regression analysis this usually takes the form of predicting a range of values that is calibrated to cover the true value 95% of the time or in the case of classification it could be just a matter of producing class probabilities.
How Much Training Data is Required for Machine Learning?
The amount of data you need depends both on the complexity of your problem and on the complexity of your chosen algorithm.
My hope that one or more of these methods may help you understand the difficulty of the question and how it is tightly coupled with the heart of the induction problem that you are trying to solve.
In practice, I answer this question myself using learning curves (see below), using resampling methods on small datasets (e.g.
This means that there needs to be enough data to reasonably capture the relationships that may exist both between input features and between input features and output features.
Use your domain knowledge, or find a domain expert and reason about the domain and the scale of data that may be required to reasonably capture the useful complexity in the problem.
k-nearest neighbors) is often contrasted against the optimal Bayesian decision rule and the difficulty is characterized in the context of the curse of dimensionality;
For example: Findings suggest avoiding local methods (like k-nearest neighbors) for sparse samples from high dimensional problems (e.g.
If a linear algorithm achieves good performance with hundreds of examples per class, you may need thousands of examples per class for a nonlinear algorithm, like random forest, or an artificial neural network.
It is common when developing a new machine learning algorithm to demonstrate and even explain the performance of the algorithm in response to the amount of data or problem complexity.
Plotting the result as a line plot with training dataset size on the x-axis and model skill on the y-axis will give you an idea of how the size of the data affects the skill of the model on your specific problem.
From this graph, you may be able to project the amount of data that is required to develop a skillful model, or perhaps how little data you actually need before hitting an inflection point of diminishing returns.
highly recommend this approach in general in order to develop robust models in the context of a well-rounded understanding of the problem.
If pressed with the question, and with zero knowledge of the specifics of your problem, I would say something naive like: Again, this is just more ad hoc guesstimating, but it’s a starting point if you need it.
For example, simple statistical machine translation: If you are performing traditional predictive modeling, then there will likely be a point of diminishing returns in the training set size, and you should study your problems and your chosen model/s to see where that point is.
Learn something, then take action to better understand what you have with further analysis, extend the data you have with augmentation, or gather more data from your domain.
In this post, you discovered a suite of ways to think and reason about the problem of answering the common question: How much training data do I need for machine learning?
- On Thursday, September 19, 2019
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