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1. Azure Ml Cheat Sheet
2. Ml Cheat Sheet Logistic Regression
3. Ml Cheat Sheet Scikit

A common question is “Which machine learning algorithm should I use?” The algorithm you select depends primarily on two different aspects of your data science scenario:

• What you want to do with your data? Specifically, what is the business question you want to answer by learning from your past data?

• What are the requirements of your data science scenario? Specifically, what is the accuracy, training time, linearity, number of parameters, and number of features your solution supports?

Business scenarios and the Machine Learning Algorithm Cheat Sheet

The Azure Machine Learning Algorithm Cheat Sheet helps you with the first consideration: What you want to do with your data? On the Machine Learning Algorithm Cheat Sheet, look for task you want to do, and then find a Azure Machine Learning designer algorithm for the predictive analytics solution.

Machine Learning designer provides a comprehensive portfolio of algorithms, such as Multiclass Decision Forest, Recommendation systems, Neural Network Regression, Multiclass Neural Network, and K-Means Clustering. Each algorithm is designed to address a different type of machine learning problem. See the Machine Learning designer algorithm and module reference for a complete list along with documentation about how each algorithm works and how to tune parameters to optimize the algorithm.

Note

To download the machine learning algorithm cheat sheet, go to Azure Machine learning algorithm cheat sheet.

Along with guidance in the Azure Machine Learning Algorithm Cheat Sheet, keep in mind other requirements when choosing a machine learning algorithm for your solution. Following are additional factors to consider, such as the accuracy, training time, linearity, number of parameters and number of features.

Comparison of machine learning algorithms

Some learning algorithms make particular assumptions about the structure of the data or the desired results. If you can find one that fits your needs, it can give you more useful results, more accurate predictions, or faster training times.

The following table summarizes some of the most important characteristics of algorithms from the classification, regression, and clustering families:

Requirements for a data science scenario

Once you know what you want to do with your data, you need to determine additional requirements for your solution.

Make choices and possibly trade-offs for the following requirements:

• Accuracy
• Training time
• Linearity
• Number of parameters
• Number of features

Accuracy

Accuracy in machine learning measures the effectiveness of a model as the proportion of true results to total cases. In Machine Learning designer, the Evaluate Model module computes a set of industry-standard evaluation metrics. You can use this module to measure the accuracy of a trained model.

Getting the most accurate answer possible isn’t always necessary. Sometimes an approximation is adequate, depending on what you want to use it for. If that is the case, you may be able to cut your processing time dramatically by sticking with more approximate methods. Approximate methods also naturally tend to avoid overfitting.

There are three ways to use the Evaluate Model module:

• Generate scores over your training data in order to evaluate the model
• Generate scores on the model, but compare those scores to scores on a reserved testing set
• Compare scores for two different but related models, using the same set of data

For a complete list of metrics and approaches you can use to evaluate the accuracy of machine learning models, see Evaluate Model module.

Training time

In supervised learning, training means using historical data to build a machine learning model that minimizes errors. The number of minutes or hours necessary to train a model varies a great deal between algorithms. Training time is often closely tied to accuracy; one typically accompanies the other.

In addition, some algorithms are more sensitive to the number of data points than others. You might choose a specific algorithm because you have a time limitation, especially when the data set is large.

In Machine Learning designer, creating and using a machine learning model is typically a three-step process:

1. Configure a model, by choosing a particular type of algorithm, and then defining its parameters or hyperparameters.

2. Provide a dataset that is labeled and has data compatible with the algorithm. Connect both the data and the model to Train Model module.

3. After training is completed, use the trained model with one of the scoring modules to make predictions on new data.

Linearity

Linearity in statistics and machine learning means that there is a linear relationship between a variable and a constant in your dataset. For example, linear classification algorithms assume that classes can be separated by a straight line (or its higher-dimensional analog).

Lots of machine learning algorithms make use of linearity. In Azure Machine Learning designer, they include:

Linear regression algorithms assume that data trends follow a straight line. This assumption isn't bad for some problems, but for others it reduces accuracy. Despite their drawbacks, linear algorithms are popular as a first strategy. They tend to be algorithmically simple and fast to train.

Nonlinear class boundary: Relying on a linear classificationalgorithm would result in low accuracy.

Azure Ml Cheat Sheet

Data with a nonlinear trend: Using a linear regression method wouldgenerate much larger errors than necessary.

Number of parameters

Parameters are the knobs a data scientist gets to turn when setting up an algorithm. They are numbers that affect the algorithm’s behavior, such as error tolerance or number of iterations, or options between variants of how the algorithm behaves. The training time and accuracy of the algorithm can sometimes be sensitive to getting just the right settings. Typically, algorithms with large numbers of parameters require the most trial and error to find a good combination.

Alternatively, there is the Tune Model Hyperparameters module in Machine Learning designer: The goal of this module is to determine the optimum hyperparameters for a machine learning model. The module builds and tests multiple models by using different combinations of settings. It compares metrics over all models to get the combinations of settings.

While this is a great way to make sure you’ve spanned the parameter space, the time required to train a model increases exponentially with the number of parameters. The upside is that having many parameters typically indicates that an algorithm has greater flexibility. It can often achieve very good accuracy, provided you can find the right combination of parameter settings.

Number of features

In machine learning, a feature is a quantifiable variable of the phenomenon you are trying to analyze. For certain types of data, the number of features can be very large compared to the number of data points. This is often the case with genetics or textual data.

A large number of features can bog down some learning algorithms, making training time unfeasibly long. Support vector machines are particularly well suited to scenarios with a high number of features. For this reason, they have been used in many applications from information retrieval to text and image classification. Support vector machines can be used for both classification and regression tasks.

Feature selection refers to the process of applying statistical tests to inputs, given a specified output. The goal is to determine which columns are more predictive of the output. The Filter Based Feature Selection module in Machine Learning designer provides multiple feature selection algorithms to choose from. The module includes correlation methods such as Pearson correlation and chi-squared values.

You can also use the Permutation Feature Importance module to compute a set of feature importance scores for your dataset. You can then leverage these scores to help you determine the best features to use in a model.

Next steps

• For descriptions of all the machine learning algorithms available in Azure Machine Learning designer, see Machine Learning designer algorithm and module reference
• To explore the relationship between deep learning, machine learning, and AI, see Deep Learning vs. Machine Learning

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.

A 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:

• The size, quality, and nature of data.
• The available computational time.
• The urgency of the task.
• What you want to do with the data.

Even an experienced data scientist cannot tell which algorithm will perform the best before trying different algorithms. We are not advocating a one-and-done approach, but we do hope to provide some guidance on which algorithms to try first depending on some clear factors.

Editor's note: This post was originally published in 2017. We are republishing it with an updated video tutorial on this topic. You can watch How to Choose a Machine Learning Algorithm below. Or keep reading to find a cheat sheet that helps you find the right algorithm for your project.

The machine learning algorithm cheat sheet

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. This article walks you through the process of how to use the sheet.

Since the cheat sheet is designed for beginner data scientists and analysts, we will make some simplified assumptions when talking about the algorithms.

The algorithms recommended here result from compiled feedback and tips from several data scientists and machine learning experts and developers. There are several issues on which we have not reached an agreement and for these issues, we try to highlight the commonality and reconcile the difference.

Additional algorithms will be added in later as our library grows to encompass a more complete set of available methods.

How to use the cheat sheet

Read the path and algorithm labels on the chart as 'If <path label> then use <algorithm>.' For example:

• If you want to perform dimension reduction then use principal component analysis.
• If you need a numeric prediction quickly, use decision trees or linear regression.
• If you need a hierarchical result, use hierarchical clustering.

Sometimes more than one branch will apply, and other times none of them will be a perfect match. It’s important to remember these paths are intended to be rule-of-thumb recommendations, so some of the recommendations are not exact. Several data scientists I talked with said that the only sure way to find the very best algorithm is to try all of them.

Types of machine learning algorithms

This section provides an overview of the most popular types of machine learning. If you’re familiar with these categories and want to move on to discussing specific algorithms, you can skip this section and go to “When to use specific algorithms” below.

Supervised learning

Supervised learning algorithms make predictions based on a set of examples. For example, historical sales can be used to estimate future prices. With supervised learning, you have an input variable that consists of labeled training data and a desired output variable. You use an algorithm to analyze the training data to learn the function that maps the input to the output. This inferred function maps new, unknown examples by generalizing from the training data to anticipate results in unseen situations.

• Classification: When the data are being used to predict a categorical variable, supervised learning is also called classification. This is the case when assigning a label or indicator, either dog or cat to an image. When there are only two labels, this is called binary classification. When there are more than two categories, the problems are called multi-class classification.
• Regression: When predicting continuous values, the problems become a regression problem.
• Forecasting: This is the process of making predictions about the future based on past and present data. It is most commonly used to analyze trends. A common example might be an estimation of the next year sales based on the sales of the current year and previous years.

Semi-supervised learning

The challenge with supervised learning is that labeling data can be expensive and time-consuming. If labels are limited, you can use unlabeled examples to enhance supervised learning. Because the machine is not fully supervised in this case, we say the machine is semi-supervised. With semi-supervised learning, you use unlabeled examples with a small amount of labeled data to improve the learning accuracy.

Unsupervised learning

When performing unsupervised learning, the machine is presented with totally unlabeled data. It is asked to discover the intrinsic patterns that underlie the data, such as a clustering structure, a low-dimensional manifold, or a sparse tree and graph.

• Clustering: Grouping a set of data examples so that examples in one group (or one cluster) are more similar (according to some criteria) than those in other groups. This is often used to segment the whole dataset into several groups. Analysis can be performed in each group to help users to find intrinsic patterns.
• Dimension reduction: Reducing the number of variables under consideration. In many applications, the raw data have very high dimensional features and some features are redundant or irrelevant to the task. Reducing the dimensionality helps to find the true, latent relationship.

Reinforcement learning

Reinforcement learning is another branch of machine learning which is mainly utilized for sequential decision-making problems. In this type of machine learning, unlike supervised and unsupervised learning, we do not need to have any data in advance; instead, the learning agent interacts with an environment and learns the optimal policy on the fly based on the feedback it receives from that environment. Specifically, in each time step, an agent observes the environment’s state, chooses an action, and observes the feedback it receives from the environment. The feedback from an agent’s action has many important components. One component is the resulting state of the environment after the agent has acted on it. Another component is the reward (or punishment) that the agent receives from performing that particular action in that particular state. The reward is carefully chosen to align with the objective for which we are training the agent. Using the state and reward, the agent updates its decision-making policy to optimize its long-term reward. With the recent advancements of deep learning, reinforcement learning gained significant attention since it demonstrated striking performances in a wide range of applications such as games, robotics, and control. To see reinforcement learning models such as Deep-Q and Fitted-Q networks in action, check out this article.

Considerations when choosing an algorithm

When choosing an algorithm, always take these aspects into account: accuracy, training time and ease of use. Many users put the accuracy first, while beginners tend to focus on algorithms they know best.

When presented with a dataset, the first thing to consider is how to obtain results, no matter what those results might look like. Beginners tend to choose algorithms that are easy to implement and can obtain results quickly. This works fine, as long as it is just the first step in the process. 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.

When to use specific algorithms

Looking more closely at individual algorithms can help you understand what they provide and how they are used. These descriptions provide more details and give additional tips for when to use specific algorithms, in alignment with the cheat sheet.

Linear regression and Logistic regression

Linear regression is an approach for modeling the relationship between a continuous dependent variable (y) and one or more predictors (X). The relationship between (y) and (X) can be linearly modeled as (y=beta^TX+epsilon) Given the training examples ({x_i,y_i}_{i=1}^N), the parameter vector (beta) can be learnt.

If the dependent variable is not continuous but categorical, linear regression can be transformed to logistic regression using a logit link function. Logistic regression is a simple, fast yet powerful classification algorithm. Here we discuss the binary case where the dependent variable (y) only takes binary values ({y_iin(-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. Specifically, we will try to learn a function of the form:(p(y_i=1|x_i )=sigma(beta^T x_i )) and (p(y_i=-1|x_i )=1-sigma(beta^T x_i )). Here (sigma(x)=frac{1}{1+exp(-x)}) is a sigmoid function. Given the training examples({x_i,y_i}_{i=1}^N), the parameter vector (beta) can be learnt by maximizing the log-likelihood of (beta) given the data set.

Linear SVM and kernel SVM

Kernel tricks are used to map a non-linearly separable functions into a higher dimension linearly separable function. A support vector machine (SVM) training algorithm finds the classifier represented by the normal vector (w) and bias (b) of the hyperplane. This hyperplane (boundary) separates different classes by as wide a margin as possible. The problem can be converted into a constrained optimization problem:
begin{equation*}
begin{aligned}
& underset{w}{text{minimize}}
& & ||w||
& text{subject to}
& & y_i(w^T X_i-b) geq 1, ; i = 1, ldots, n.
end{aligned}
end{equation*}

A support vector machine (SVM) training algorithm finds the classifier represented by the normal vector and bias of the hyperplane. This hyperplane (boundary) separates different classes by as wide a margin as possible. The problem can be converted into a constrained optimization problem:

Kernel tricks are used to map a non-linearly separable functions into a higher dimension linearly separable function.

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.

When most dependent variables are numeric, logistic regression and SVM should be the first try for classification. These models are easy to implement, their parameters easy to tune, and the performances are also pretty good. So these models are appropriate for beginners.

Trees and ensemble trees

Decision trees, random forest and gradient boosting are all algorithms based on decision trees. Dvr1000 driver download. 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. Decision trees are easy to understand and implement. However, they tend to over-fit data when we exhaust the branches and go very deep with the trees. Random Forrest and gradient boosting are two popular ways to use tree algorithms to achieve good accuracy as well as overcoming the over-fitting problem.

Neural networks and deep learning

A convolution neural network architecture (image source: wikipedia creative commons)

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. Increasingly powerful computational capabilities, such as graphical processing unit (GPU) and massively parallel processing (MPP), have also spurred the revived adoption of neural networks. The resurgent research in neural networks has given rise to the invention of models with thousands of layers.

In other words, shallow neural networks have evolved into deep learning neural networks. Deep neural networks have been very successful for supervised learning. When used for speech and image recognition, deep learning performs as well as, or even better than, humans. Applied to unsupervised learning tasks, such as feature extraction, deep learning also extracts features from raw images or speech with much less human intervention.

A neural network consists of three parts: input layer, hidden layers and output layer. The training samples define the input and output layers. When the output layer is a categorical variable, then the neural network is a way to address classification problems. When the output layer is a continuous variable, then the network can be used to do regression. When the output layer is the same as the input layer, the network can be used to extract intrinsic features. The number of hidden layers defines the model complexity and modeling capacity.

k-means/k-modes, GMM (Gaussian mixture model) clustering

Ml Cheat Sheet Logistic Regression

Kmeans/k-modes, GMM clustering aims to partition n observations into k clusters. K-means define hard assignment: the samples are to be and only to be associated to one cluster. GMM, however, defines a soft assignment for each sample. Each sample has a probability to be associated with each cluster. Both algorithms are simple and fast enough for clustering when the number of clusters k is given.

DBSCAN

When the number of clusters k is not given, DBSCAN (density-based spatial clustering) can be used by connecting samples through density diffusion.

Hierarchical clustering

Hierarchical partitions can be visualized using a tree structure (a dendrogram). It does not need the number of clusters as an input and the partitions can be viewed at different levels of granularities (i.e., can refine/coarsen clusters) using different K.

PCA, SVD and LDA

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. Principal component analysis (PCA), singular value decomposition (SVD), andlatent Dirichlet allocation (LDA) all can be used to perform dimension reduction.

PCA is an unsupervised clustering method that maps the original data space into a lower-dimensional space while preserving as much information as possible. The PCA basically finds a subspace that most preserve the data variance, with the subspace defined by the dominant eigenvectors of the data’s covariance matrix.

Ml Cheat Sheet Scikit

The SVD is related to PCA in the sense that the SVD of the centered data matrix (features versus samples) provides the dominant left singular vectors that define the same subspace as found by PCA. However, SVD is a more versatile technique as it can also do things that PCA may not do. For example, the SVD of a user-versus-movie matrix is able to extract the user profiles and movie profiles that can be used in a recommendation system. In addition, SVD is also widely used as a topic modeling tool, known as latent semantic analysis, in natural language processing (NLP).

A related technique in NLP is latent Dirichlet allocation (LDA). Gfm driver. LDA is a probabilistic topic model and it decomposes documents into topics in a similar way as a Gaussian mixture model (GMM) decomposes continuous data into Gaussian densities. Differently from the GMM, an LDA models discrete data (words in documents) and it constrains that the topics are a priori distributed according to a Dirichlet distribution.

Conclusions

This is the work flow which is easy to follow. The takeaway messages when trying to solve a new problem are:

• Define the problem. What problems do you want to solve?
• Start simple. Be familiar with the data and the baseline results.
• Then try something more complicated.

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