AI News, Modern Machine Learning Algorithms: Strengths and Weaknesses

Modern Machine Learning Algorithms: Strengths and Weaknesses

In this guide, we&#8217;ll take a practical, concise tour through modern machine learning algorithms.

For example, Scikit-Learn&#8217;s documentation page groups algorithms by their learning mechanism. This produces categories such as: However, from our experience, this isn&#8217;t always the most practical way to group algorithms.

That&#8217;s because for applied machine learning, you&#8217;re usually not thinking, &#8220;boy do I want to train a support vector machine today!&#8221;

Of course, the algorithms you try must be appropriate for your problem, which is where picking the right machine learning task comes in.

As an analogy, if you need to clean your house, you might use a vacuum, a broom, or a mop, but you wouldn't bust out a shovel and start digging.

They are: In Part 2, we will cover dimensionality reduction, including: Two notes before continuing: Regression is the supervised learning task for modeling and predicting continuous, numeric variables. Examples include predicting real-estate prices, stock price movements, or student test scores.

decision trees) learn in a hierarchical fashion by repeatedly splitting your dataset into separate branches that maximize the information gain of each split.

We won't go into their underlying mechanics here, but in practice, RF's often perform very well out-of-the-box while GBM's are harder to tune but tend to have higher performance ceilings.

They use 'hidden layers' between inputs and outputs in order to model intermediary representations of the data that other algorithms cannot easily learn.

However, deep learning still requires much more data to train compared to other algorithms because the models have orders of magnitudes more parameters to estimate.

These algorithms are memory-intensive, perform poorly for high-dimensional data, and require a meaningful distance function to calculate similarity.

Examples include predicting employee churn, email spam, financial fraud, or student letter grades.

Predictions are mapped to be between 0 and 1 through the logistic function, which means that predictions can be interpreted as class probabilities.

The models themselves are still 'linear,' so they work well when your classes are linearly separable (i.e. they can be separated by a single decision surface).

To predict a new observation, you'd simply 'look up' the class probabilities in your 'probability table' based on its feature values.

However, we want to leave you with a few words of advice based on our experience: If you'd like to learn more about the applied machine learning workflow and how to efficiently train professional-grade models, we invite you to check out our Data Science Primer.

For more over-the-shoulder guidance, we also offer a comprehensive masterclass that further explains the intuition behind many of these algorithms and teaches you how to apply them to real-world problems.

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