AI News, The Machine Learning Cheatsheet

The Machine Learning Cheatsheet

Lately, I spent some time on various data science projects: predictive analysis, natural language processing, graph analysis, etc.

Of course, those models barely represent 10 lines of code in my notebooks, thanks to the wonderful open-source libraries accessible today.

The idea with this project is to create a simple, concise, potentially exhaustive document about the most common machine learning algorithms.

This cheatsheet is meant to be a constant work in progress, so please feel free to contact me for any possible improvement!

Which machine learning algorithm should I use?

This resource is designed primarily for beginner to intermediate data scientists or analysts who are interested in identifying and applying machine learning algorithms to address the problems of their interest.

typical question asked by a beginner, when facing a wide variety of machine learning algorithms, is “which algorithm should I use?” The answer to the question varies depending on many factors, including: Even an experienced data scientist cannot tell which algorithm will perform the best before trying different algorithms.

The machine learning algorithm cheat sheet helps you to choose from a variety of machine learning algorithms to find the appropriate algorithm for your specific problems.

Reinforcement learning analyzes and optimizes the behavior of an agent based on the feedback from the environment.  Machines try different scenarios to discover which actions yield the greatest reward, rather than being told which actions to take.

Once you obtain some results and become familiar with the data, you may spend more time using more sophisticated algorithms to strengthen your understanding of the data, hence further improving the results.

Even in this stage, the best algorithms might not be the methods that have achieved the highest reported accuracy, as an algorithm usually requires careful tuning and extensive training to obtain its best achievable performance.

Here we discuss the binary case where the dependent variable \(y\)y only takes binary values \(\{y_i\in(-1,1)\}_{i=1}^N\)\{y_i\in(-1,1)\}_{i=1}^N (it which can be easily extended to multi-class classification problems).

In logistic regression we use a different hypothesis class to try to predict the probability that a given example belongs to the '1' class versus the probability that it belongs to the '-1' class.

A support vector machine (SVM) training algorithm finds the classifier represented by the normal vector \(w\)w and bias \(b\)b of the hyperplane.

support vector machine (SVM) training algorithm finds the classifier represented by the normal vector  and bias  of the hyperplane.

The problem can be converted into a constrained optimization problem: When the classes are not linearly separable, a kernel trick can be used to map a non-linearly separable space into a higher dimension linearly separable space.

Decision trees, random forest and gradient boosting are all algorithms based on decision trees.  There are many variants of decision trees, but they all do the same thing – subdivide the feature space into regions with mostly the same label.

Neural networks flourished in the mid-1980s due to their parallel and distributed processing ability.  But research in this field was impeded by the ineffectiveness of the back-propagation training algorithm that is widely used to optimize the parameters of neural networks.

Support vector machines (SVM) and other simpler models, which can be easily trained by solving convex optimization problems, gradually replaced neural networks in machine learning.

In recent years, new and improved training techniques such as unsupervised pre-training and layer-wise greedy training have led to a resurgence of interest in neural networks.

neural network consists of three parts: input layer, hidden layers and output layer.  The training samples define the input and output layers.

We generally do not want to feed a large number of features directly into a machine learning algorithm since some features may be irrelevant or the “intrinsic” dimensionality may be smaller than the number of features.

The SVD is related to PCA in the sense that SVD of the centered data matrix (features versus samples) provides the dominant left singular vectors that define the same subspace as found by PCA.

The takeaway messages when trying to solve a new problem are: SAS Visual Data Mining and Machine Learning provides a  good platform for beginners to learn machine learning and apply machine learning methods to their problems.

How to choose algorithms for Azure Machine Learning Studio

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 Azure Machine Learning Studio 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

Cheatsheet – Python & R codes for common Machine Learning Algorithms

In the end, they give up on machine learning by saying it is very computation heavy or it is very difficult or I can’t improve my models above a threshold –

Today’s cheat sheet aims to change a few Data Darby’s to machine learning advocates. Here’s a collection of 10 most commonly used machine learning algorithms with their codes in Python and R.

Considering the rising usage of machine learning in building models, this cheat sheet is good to act as a code guide to help you bring these machine learning algorithms to use.

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