AI News, How to choose algorithms for Microsoft Azure Machine Learning

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

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

Perceptron

In machine learning, the perceptron is an algorithm for supervised learning of binary classifiers (functions that can decide whether an input, represented by a vector of numbers, belongs to some specific class or not).[1] It is a type of linear classifier, i.e.

a classification algorithm that makes its predictions based on a linear predictor function combining a set of weights with the feature vector.

The perceptron algorithm was invented in 1957 at the Cornell Aeronautical Laboratory by Frank Rosenblatt,[3] funded by the United States Office of Naval Research.[4] The perceptron was intended to be a machine, rather than a program, and while its first implementation was in software for the IBM 704, it was subsequently implemented in custom-built hardware as the 'Mark 1 perceptron'.

Weights were encoded in potentiometers, and weight updates during learning were performed by electric motors.[2]:193 In a 1958 press conference organized by the US Navy, Rosenblatt made statements about the perceptron that caused a heated controversy among the fledgling AI community;

based on Rosenblatt's statements, The New York Times reported the perceptron to be 'the embryo of an electronic computer that [the Navy] expects will be able to walk, talk, see, write, reproduce itself and be conscious of its existence.'[4] Although the perceptron initially seemed promising, it was quickly proved that perceptrons could not be trained to recognise many classes of patterns.

This caused the field of neural network research to stagnate for many years, before it was recognised that a feedforward neural network with two or more layers (also called a multilayer perceptron) had far greater processing power than perceptrons with one layer (also called a single layer perceptron).[dubious – discuss] Single layer perceptrons are only capable of learning linearly separable patterns;

(See the page on Perceptrons (book) for more information.) Three years later Stephen Grossberg published a series of papers introducing networks capable of modelling differential, contrast-enhancing and XOR functions.

The kernel perceptron algorithm was already introduced in 1964 by Aizerman et al.[5] Margin bounds guarantees were given for the Perceptron algorithm in the general non-separable case first by Freund and Schapire (1998),[1] and more recently by Mohri and Rostamizadeh (2013) who extend previous results and give new L1 bounds.[6] The perceptron is a simplified model of a biological neuron.

While the complexity of biological neuron models is often required to fully understand neural behavior, research suggests a perceptron-like linear model can produce some behavior seen in real neurons [7][8].

In the modern sense, the perceptron is an algorithm for learning a binary classifier: a function that maps its input x (a real-valued vector) to an output value

i

=

1

m

i

i

The solution spaces of decision boundaries for all binary functions and learning behaviors are studied in the reference.[9] In the context of neural networks, a perceptron is an artificial neuron using the Heaviside step function as the activation function.

The perceptron algorithm is also termed the single-layer perceptron, to distinguish it from a multilayer perceptron, which is a misnomer for a more complicated neural network.

This is because multiplying the update by any constant simply rescales the weights but never changes the sign of the prediction.[10] The algorithm updates the weights after steps 2a and 2b.

These weights are immediately applied to a pair in the training set, and subsequently updated, rather than waiting until all pairs in the training set have undergone these steps.

The perceptron is a linear classifier, therefore it will never get to the state with all the input vectors classified correctly if the training set D is not linearly separable, i.e.

In this case, no 'approximate' solution will be gradually approached under the standard learning algorithm, but instead learning will fail completely.

x

j

j

x

j

j

2

2

The idea of the proof is that the weight vector is always adjusted by a bounded amount in a direction with which it has a negative dot product, and thus can be bounded above by O(√t), where t is the number of changes to the weight vector.

However, it can also be bounded below by O(t) because if there exists an (unknown) satisfactory weight vector, then every change makes progress in this (unknown) direction by a positive amount that depends only on the input vector.

While the perceptron algorithm is guaranteed to converge on some solution in the case of a linearly separable training set, it may still pick any solution and problems may admit many solutions of varying quality.[11] The perceptron of optimal stability, nowadays better known as the linear support vector machine, was designed to solve this problem (Krauth and Mezard, 1987)[12].

The pocket algorithm with ratchet (Gallant, 1990) solves the stability problem of perceptron learning by keeping the best solution seen so far 'in its pocket'.

However, these solutions appear purely stochastically and hence the pocket algorithm neither approaches them gradually in the course of learning, nor are they guaranteed to show up within a given number of learning steps.

The Maxover algorithm (Wendemuth, 1995)[13] is 'robust' in the sense that it will converge regardless of (prior) knowledge of linear separability of the data set.

In the linearly separable case, it will solve the training problem – if desired, even with optimal stability (maximum margin between the classes).

The algorithm starts a new perceptron every time an example is wrongly classified, initializing the weights vector with the final weights of the last perceptron.

Each perceptron will also be given another weight corresponding to how many examples do they correctly classify before wrongly classifying one, and at the end the output will be a weighted vote on all perceptron.

The so-called perceptron of optimal stability can be determined by means of iterative training and optimization schemes, such as the Min-Over algorithm (Krauth and Mezard, 1987)[12] or the AdaTron (Anlauf and Biehl, 1989)) .[14] AdaTron uses the fact that the corresponding quadratic optimization problem is convex.

-perceptron further used a pre-processing layer of fixed random weights, with thresholded output units.

Another way to solve nonlinear problems without using multiple layers is to use higher order networks (sigma-pi unit).

In this type of network, each element in the input vector is extended with each pairwise combination of multiplied inputs (second order).

Indeed, if we had the prior constraint that the data come from equi-variant Gaussian distributions, the linear separation in the input space is optimal, and the nonlinear solution is overfitted.

Learning again iterates over the examples, predicting an output for each, leaving the weights unchanged when the predicted output matches the target, and changing them when it does not.

a

r

g

m

a

x

In recent years, perceptron training has become popular in the field of natural language processing for such tasks as part-of-speech tagging and syntactic parsing (Collins, 2002).

Linear Regression for Machine Learning

Linear regression is perhaps one of the most well known and well understood algorithms in statistics and machine learning.

Machine learning, more specifically the field of predictive modeling is primarily concerned with minimizing the error of a model or making the most accurate predictions possible, at the expense of explainability.

In applied machine learning we will borrow, reuse and steal algorithms from many different fields, including statistics and use them towards these ends.

As such, linear regression was developed in the field of statistics and is studied as a model for understanding the relationship between input and output numerical variables, but has been borrowed by machine learning.

Now that we know some names used to describe linear regression, let’s take a closer look at the representation used.

The representation is a linear equation that combines a specific set of input values (x) the solution to which is the predicted output for that set of input values (y).

The linear equation assigns one scale factor to each input value or column, called a coefficient and represented by the capital Greek letter Beta (B).

= B0 + B1*x In higher dimensions when we have more than one input (x), the line is called a plane or a hyper-plane. The representation therefore is the form of the equation and the specific values used for the coefficients (e.g.

When a coefficient becomes zero, it effectively removes the influence of the input variable on the model and therefore from the prediction made from the model (0 * x = 0).

This becomes  relevant if you look at regularization methods that change the learning algorithm to reduce the complexity of regression models by putting pressure on the absolute size of the coefficients, driving some to zero.

Now that we understand the representation used for a linear regression model, let’s review some ways that we can learn this representation from data.

Learning a linear regression model means estimating the values of the coefficients used in the representation with the data that we have available.

This means that given a regression line through the data we calculate the distance from each data point to the regression line, square it, and sum all of the squared errors together.

When using this method, you must select a learning rate (alpha) parameter that determines the size of the improvement step to take on each iteration of the procedure.

These seek to both minimize the sum of the squared error of the model on the training data (using ordinary least squares) but also to reduce the complexity of the model (like the number or absolute size of the sum of all coefficients in the model).

Two popular examples of regularization procedures for linear regression are: These methods are effective to use when there is collinearity in your input values and ordinary least squares would overfit the training data.

Now that you know some techniques to learn the coefficients in a linear regression model, let’s look at how we can use a model to make predictions on new data.

weight = 0.1 + 0.05 * 182 weight = 91.1 You can see that the above equation could be plotted as a line in two-dimensions.

Now that we know how to make predictions given a learned linear regression model, let’s look at some rules of thumb for preparing our data to make the most of this type of model.

Linear Regression - Machine Learning Fun and Easy

Linear Regression - Machine Learning Fun and Easy

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