AI News, BOOK REVIEW: Comparing supervised learning algorithms

Comparing supervised learning algorithms

In the data science course that I instruct, we cover most of the data science pipeline but focus especially on machine learning.

Near the end of this 11-week course, we spend a few hours reviewing the material that has been covered throughout the course, with the hope that students will start to construct mental connections between all of the different things they have learned.

decided to create a game for the students, in which I gave them a blank table listing the supervised learning algorithms we covered and asked them to compare the algorithms across a dozen different dimensions.

realize that the characteristics and relative performance of each algorithm can vary based upon the particulars of the data (and how well it is tuned), and thus some may argue that attempting to construct an 'objective' comparison is an ill-advised task.

Comparing supervised learning algorithms

In the data science course that I instruct, we cover most of the data science pipeline but focus especially on machine learning.

Near the end of this 11-week course, we spend a few hours reviewing the material that has been covered throughout the course, with the hope that students will start to construct mental connections between all of the different things they have learned.

decided to create a game for the students, in which I gave them a blank table listing the supervised learning algorithms we covered and asked them to compare the algorithms across a dozen different dimensions.

realize that the characteristics and relative performance of each algorithm can vary based upon the particulars of the data (and how well it is tuned), and thus some may argue that attempting to construct an 'objective' comparison is an ill-advised task.

A Tour of Machine Learning Algorithms

In this post, we take a tour of the most popular machine learning algorithms.

There are different ways an algorithm can model a problem based on its interaction with the experience or environment or whatever we want to call the input data.

There are only a few main learning styles or learning models that an algorithm can have and we’ll go through them here with a few examples of algorithms and problem types that they suit.

This taxonomy or way of organizing machine learning algorithms is useful because it forces you to think about the roles of the input data and the model preparation process and select one that is the most appropriate for your problem in order to get the best result.

Let’s take a look at three different learning styles in machine learning algorithms: Input data is called training data and has a known label or result such as spam/not-spam or a stock price at a time.

hot topic at the moment is semi-supervised learning methods in areas such as image classification where there are large datasets with very few labeled examples.

The most popular regression algorithms are: Instance-based learning model is a decision problem with instances or examples of training data that are deemed important or required to the model.

Such methods typically build up a database of example data and compare new data to the database using a similarity measure in order to find the best match and make a prediction.

The most popular instance-based algorithms are: An extension made to another method (typically regression methods) that penalizes models based on their complexity, favoring simpler models that are also better at generalizing.

The most popular regularization algorithms are: Decision tree methods construct a model of decisions made based on actual values of attributes in the data.

All methods are concerned with using the inherent structures in the data to best organize the data into groups of maximum commonality.

The most popular clustering algorithms are: Association rule learning methods extract rules that best explain observed relationships between variables in data.

The most popular association rule learning algorithms are: Artificial Neural Networks are models that are inspired by the structure and/or function of biological neural networks.

They are a class of pattern matching that are commonly used for regression and classification problems but are really an enormous subfield comprised of hundreds of algorithms and variations for all manner of problem types.

The most popular artificial neural network algorithms are: Deep Learning methods are a modern update to Artificial Neural Networks that exploit abundant cheap computation.

They are concerned with building much larger and more complex neural networks and, as commented on above, many methods are concerned with semi-supervised learning problems where large datasets contain very little labeled data.

The most popular deep learning algorithms are: Like clustering methods, dimensionality reduction seek and exploit the inherent structure in the data, but in this case in an unsupervised manner or order to summarize or describe data using less information.

Ensemble methods are models composed of multiple weaker models that are independently trained and whose predictions are combined in some way to make the overall prediction.

How to choose algorithms for Microsoft Azure Machine Learning

The answer to the question "What machine learning algorithm should I use?"

It depends on how the math of the algorithm was translated into instructions for the computer you are using.

Even the most experienced data scientists can't tell which algorithm will perform best before trying them.

The Microsoft Azure Machine Learning Algorithm Cheat Sheet helps you choose the right machine learning algorithm for your predictive analytics solutions from the Microsoft Azure Machine Learning library of algorithms. This

This cheat sheet has a very specific audience in mind: a beginning data scientist with undergraduate-level machine learning, trying to choose an algorithm to start with in Azure Machine Learning Studio.

That means that it makes some generalizations and oversimplifications, but it points you in a safe direction.

As Azure Machine Learning grows to encompass a more complete set of available methods, we'll add them.

These recommendations are compiled feedback and tips from many data scientists and machine learning experts.

We didn't agree on everything, but I've tried to harmonize our opinions into a rough consensus.

data scientists I talked with said that the only sure way to find

Supervised learning algorithms make predictions based on a set of examples.

For instance, historical stock prices can be used to hazard guesses

company's financial data, the type of industry, the presence of disruptive

it uses that pattern to make predictions for unlabeled testing data—tomorrow's

Supervised learning is a popular and useful type of machine learning.

In unsupervised learning, data points have no labels associated with them.

grouping it into clusters or finding different ways of looking at complex

In reinforcement learning, the algorithm gets to choose an action in response

signal a short time later, indicating how good the decision was. Based

where the set of sensor readings at one point in time is a data

The number of minutes or hours necessary to train a model varies a great deal

time is limited it can drive the choice of algorithm, especially when

regression algorithms assume that data trends follow a straight line.

These assumptions aren't bad for some problems, but on others they bring

Non-linear class boundary - relying on a linear classification algorithm

Data with a nonlinear trend - using a linear regression method would generate

much larger errors than necessary Despite their dangers, linear algorithms are very popular as a first line

Parameters are the knobs a data scientist gets to turn when setting up an

as error tolerance or number of iterations, or options between variants

to make sure you've spanned the parameter space, the time required to

train a model increases exponentially with the number of parameters.

For certain types of data, the number of features can be very large compared

algorithms, making training time unfeasibly long.

Some learning algorithms make particular assumptions about the structure of

- shows excellent accuracy, fast training times, and the use of linearity ○

- shows good accuracy and moderate training times As mentioned previously, linear regression fits

curve instead of a straight line makes it a natural fit for dividing

logistic regression to two-class data with just one feature - the class

boundary is the point at which the logistic curve is just as close to both classes Decision forests (regression, two-class, and multiclass), decision

all based on decision trees, a foundational machine learning concept.

decision tree subdivides a feature space into regions of roughly uniform

values Because a feature space can be subdivided into arbitrarily small regions,

it's easy to imagine dividing it finely enough to have one data point

a large set of trees are constructed with special mathematical care

memory at the expense of a slightly longer training time.

Boosted decision trees avoid overfitting by limiting how many times they can

a variation of decision trees for the special case where you want to know

that input features are passed forward (never backward) through a sequence

a long time to train, particularly for large data sets with lots of features.

typical support vector machine class boundary maximizes the margin separating

Any new data points that fall far outside that boundary

PCA-based anomaly detection - the vast majority of the data falls into

data set is grouped into five clusters using K-means There is also an ensemble one-v-all multiclass classifier, which

Machine Learning Crash Course, Part I: Supervised Machine Learning

A few notable quotes include: Addressing each quote in order: With all the nonsense the media uses to describe machine learning (ML) and artificial intelligence (AI), it’s time we do a deep dive into what these technologies actually do.

Instead, a machine learning program might say something like, “examine the last 1000 games of checkers I’ve played and pick the move that maximizes the probability that I will win the game”.

In this article, we’ll cover just the first of the three.  Supervised learning algorithms try to find a formula that accurately predicts the output label from input variables.

The table below lists the dollars spent on TV ads and the resulting sales from 200 advertising campaigns.  First, we feed the historical data into our linear regression model.

This produces a mathematical formula that predicts sales based on our input variable, TV ad spending: Sales = 7.03 + 0.047(TV) In the above graph, we have plotted both the historical data points (the black dots) as well as the formula our ML algorithm produces (the red line).

To answer our original question of expected revenue, we can simply plug $100 in for the variable TV to get, $11.73 = 7.03 + 0.047($100) In other words, after spending 100 dollars on TV advertising, we can expect to generate only $11.73 in sales, based on past data.

In summary, we used machine learning (specifically, linear regression) to predict how much revenue a TV advertising campaign would generate, based on historical data.  In the previous example, we mapped a numeric input (TV ad spending) to a numeric output (sales).

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