mastering machine learning algorithms packt pdf

In the following diagram, we see a schematic representation of the process: In this way, we can assess the accuracy of the model using different sampling splits, and the training process can be performed on larger datasets; in particular, on (k-1)N samples. Whenever an additional test set is needed, it's always possible to reuse the same function: splitting the original test set into a larger component, which becomes the actual validation set, and a smaller one, the new test set that will be employed for the final performance check. Luckily, all Scikit-Learn algorithms that benefit from or need a whitening preprocessing step provide a built-in feature, so no further actions are normally required; however, for all readers who want to implement some algorithms directly, I've written two Python functions that can be used both for zero-centering and whitening. It's clear that we can never work directly with pdata; it's only possible to find a well-defined formula describing pdata in a few limited cases (for example, the distribution of all images belonging to a dataset). Conversely, robust scaling is able to produce an almost perfect normal distribution N(0, I) because the outliers are kept out of the calculations and only the central points contribute to the scaling factor. If the sample size is N, an error equal to 0 implies that there are no misclassifications, while an error equal to 1 means that all the samples have been misclassified. As discussed, animals can perform abstractions and extend the concepts learned in a particular context to similar, novel contexts. For a long time, several researchers opposed perceptrons (linear neural networks) because they couldn't classify a dataset generated by the XOR function. We also need to add that we expect the sample to have polynomial growth as a function of and . Mastering Machine Learning Algorithms is your complete guide to quickly getting to grips with popular machine learning algorithms. In the following graph, it's possible to compare an original dataset and the result of whitening, which in this case is both zero-centered and with an identity covariance matrix: Original dataset (left) and whitened version (right). The first formal work on this was published by Valiant (in Valiant L., A theory of the learnable, Communications of the ACM, 27, 1984) and is mostly an introduction of the concept of Probably Approximately Correct (PAC) learning. The idea of capacity, for example, is an open-ended question that neuroscientists keep on asking themselves about the human brain. A valid method to detect the problem of wrongly selected test sets is provided by the cross-validation technique. In general, we can observe a very high training accuracy (even close to the Bayes level), but not a poor validation accuracy. Of course, when we consider our sample populations, we always need to assume that they're drawn from the original data-generating distribution. In general, these algorithms work with matrices that become symmetrical after applying the whitening. Another approach to scaling is to set the range where all features should lie. The ability to rapidly change the curvature is proportional to the degree. Animals are extremely capable at identifying critical features from a family of samples, and generalizing them to interpret new experiences (for example, a baby learns to distinguish a teddy-bear from a person after only seeing their parents and a few other people). In deep learning scenarios, a zero-centered dataset allows exploiting the symmetry of some activation function, driving to a faster convergence (we're going to discuss these details in the next chapters). Consider, for example, the following plot:  Saddle point in a bidimensional scenario. In fact, when the estimation of the parameters is biased, its expected value is always different from the true value. Download it Mastering Machine Learning In One Day books also available in PDF, EPUB, and Mobi Format for read it on your Kindle device, PC, phones or tablets. In many real cases, this is an almost impossible condition; however, it's always useful to look for convex loss functions, because they can be easily optimized through the gradient descent method. The task of a parametric learning process is to find the best parameter set that maximizes a target function, the value of which is proportional to the accuracy of the model, given specific input X and output Y datasets (or proportional to the error, if we're trying to minimize the error). More specifically, we can define a stochastic data generating process with an associated joint probability distribution: The process pdata represents the broadest and most abstract expression of the problem. Therefore, when the number of folds increases, we should expect an improvement in the performances. Independent of the number of iterations, this model will never be able to learn a good association between X and Y. Of course, this value is strictly correlated to the nature of the task and to the structure of the dataset. We can immediately understand that, in the first case, the maximum likelihood (which represents the value for which the model has the highest probability to generate the training dataset – the concept will be discussed in a dedicated section) can be easily reached using classic optimization methods, because the surface is very peaked. Therefore, we can cut them out from the computation by setting an appropriate quantile. Remember that the estimation is a function of X, and cannot be considered a constant in the sum. In general, any regular function can be employed; however, we normally need a function that can contrast the indefinite growth of the parameters. Before starting the discussion of the features of a model, it's helpful to introduce some fundamental elements related to the concept of learnability, work not too dissimilar from the mathematical definition of generic computable functions. This condition assures that, at least on average, the estimator yields results distributed around the true values. Other strategies to prevent overfitting are based on a technique called regularization, which we're going to discuss in the last part of this chapter. Machine learning has gained tremendous popularity for its powerful and fast predictions with large datasets. That's because this simple problem requires a representational capacity higher than the one provided by linear classifiers. We define the bias of an estimator in relation to a parameter : In other words, the bias of is the difference between the expected value of the estimation and the real parameter value. It's within the limits of reasonable change, for example, for a component of the model to recognize the similarities between a car and truck (for example, they both have a windshield and a radiator) and force some parameters to shift from their initial configuration, whose targets are cars, to a new configuration based on trucks. In this case, it could be useful to repeat the training process, stopping it at the epoch previous to es (where the minimum validation cost has been achieved). The goal of domain adaptation is to find the optimal methods to let a model shift from M to M' and vice versa, in order to maximize its ability to work with a specific data generating process. In the first diagram, the model is linear and has two parameters, while in the second one, it is quadratic and has three parameters. That means we can summarize the previous definition, saying that for a PAC learnable problem, . Mastering Machine Learning Algorithms - 2nd Edition. This ability is not only important but also necessary. In this way, Scikit-Learn will automatically use Stratified K-Fold for categorical classifications, and Standard K-Fold for all other cases: The accuracy is very high (> 0.9) in every fold, therefore we expect to have even higher accuracy using the LOO method. If we are training a classifier, our goal is to create a model whose distribution is as similar as possible to pdata. For example, we might want to exclude from our calculations all those features whose probability is lower than 10%. Even if some data preprocessing steps can improve the accuracy, when a model is underfitted, the only valid solution is to adopt a higher-capacity model. This condition can be achieved by minimizing the Kullback-Leibler divergence between the two distributions: In the previous expression, pM is the distribution generated by the model. This definition of capacity is quite rigorous (the reader who's interested in the all theoretical aspects can read Mohri M., Rostamizadeh A., Talwalkar A., Foundations of Machine Learning, Second edition, The MIT Press, 2018), but it can help understand the relation between the complexity of a dataset and a suitable model family. The default value for correct is True: As we have previously discussed, the numerosity of the sample available for a project is always limited. In fact, in line with the laws of probability, it's easy to verify that: A model with a high bias is likely to underfit the training set. ElasticNet can yield excellent results whenever it's necessary to mitigate overfitting effects while encouraging sparsity. In many cases, a single validation set, which is often called the test set, is used throughout the whole process. We're going to discuss this topic in Chapter 9, Neural Networks for Machine Learning. Unfortunately, sometimes the assumptions or the conditions imposed on them are not clear, and a lengthy training process can result in a complete validation failure. Large-capacity models, in particular, with small or low-informative datasets, can lead to flat likelihood surfaces with a higher probability than lower-capacity models. More formally, we can say that we want to improve our models so to get as close as possible to Bayes error, which is the theoretical minimal generalization error achievable by an estimator. We won't discuss the very technical mathematical details of PAC learning in this book, but it's useful to understand the possibility of finding a suitable way to describe a learning process, without referring to specific models. The curve lines (belonging to a classifier whose VC-capacity is greater than 3) can separate both the upper-left and the lower-right regions from the remaining space, but no straight line can do the same (while it can always separate one point from the other three). Now, if we consider a model as a parameterized function: Considering the variability of , C can be considered as a set of functions with the same structure, but different parameters: We want to determine the capacity of this model family in relation to a finite dataset X: According to the Vapnik-Chervonenkis theory, we can say that the model family C shatters X if there are no classification errors for every possible label assignment. At this point, it's possible to introduce the Cramér-Rao bound, which states that for every unbiased estimator that adopts x (with probability distribution p(x; θ)) as a measure set, the variance of any estimator of θ is always lower-bounded according to the following inequality: In fact, considering initially a generic estimator and exploiting Cauchy-Schwarz inequality with the variance and the Fisher information (which are both expressed as expected values), we obtain: Now, if we use the expression for derivatives of the bias with respect to θ, considering that the expected value of the estimation of θ doesn't depend on x, we can rewrite the right side of the inequality as: If the estimator is unbiased, the derivative on the right side is equal to zero, therefore, we get: In other words, we can try to reduce the variance, but it will be always lower-bounded by the inverse Fisher information. Key Features Develop your computer vision skills by mastering algorithms in Open Source Computer Vision 4 (OpenCV 4)and Python Apply machine learning and deep learning techniques with TensorFlow, Keras, and PyTorch Discover the modern design patterns you should avoid when developing efficient computer vision applications Book Description OpenCV is considered to be one of the best open … In the second part, we discussed the main properties of an estimator: capacity, bias, and variance. Cross-validation is a good way to assess the quality of datasets, but it can always happen that we find completely new subsets (for example, generated when the application is deployed in a production environment) that are misclassified, even if they were supposed to belong to pdata. Please login to your account first; Need help? We can conclude this section with a general rule of thumb: standard scaling is normally the first choice. To understand this concept, it's necessary to introduce an important definition: the Fisher information. All rights reserved, Access this book, plus 8,000 other titles for, Get all the quality content you’ll ever need to stay ahead with a Packt subscription – access over 8,000 online books and videos on everything in tech, Mastering Machine Learning Algorithms - Second Edition, Characteristics of a machine learning model, Contrastive Pessimistic Likelihood Estimation, Semi-supervised Support Vector Machines (S3VM), Transductive Support Vector Machines (TSVM), Label propagation based on Markov random walks, Advanced Clustering and Unsupervised Models, Clustering and Unsupervised Models for Marketing, Introduction to Market Basket Analysis with the Apriori Algorithm, Introduction to linear models for time-series, Bayesian Networks and Hidden Markov Models, Conditional probabilities and Bayes' theorem, Component Analysis and Dimensionality Reduction, Example of a deep convolutional network with TensorFlow and Keras, Introduction to Generative Adversarial Networks, Direct policy search through policy gradient, Unlock this book with a FREE 10-day trial, Instant online access to over 8,000+ books and videos, Constantly updated with 100+ new titles each month, Breadth and depth in over 1,000+ technologies, Understanding the structure and properties of good datasets, Scaling datasets, including scalar and robust scaling, Selecting training, validation and test sets, including cross-validation, Capacity, including Vapnik-Chervonenkis capacity, Variance, including overfitting and the Cramér-Rao bound, Learn to overcome the boundaries of the training set, by outputting the correct (or the most likely) outcome when new samples are presented, Otherwise, the hyperparameters are modified and the process restarts. At this point, can our model M also correctly classify the samples drawn from p2(x, y) by exploiting the analogies? Sometimes these models have only been defined from a theoretical viewpoint, but advances in research now allow us to apply machine learning concepts to better understand the behavior of complex systems such as deep neural networks. Even if we draw all the samples from the same distribution, it can happen that a randomly selected test set contains features that are not present in other training samples. Considering the shapes of the two subsets, it would be possible to say that a non-linear SVM can better capture the dynamics; however, if we sample another dataset from pdata and the diagonal tail becomes wider, logistic regression continues to classify the points correctly, while the SVM accuracy decreases dramatically. Sampling, even in the optimal case, is associated with a loss of information (unless we remove only redundancies), and therefore when creating a dataset, we always generate a bias. In order to analyze the differences, I've kept the same scale for all the diagrams. Demystify the complexity of machine learning techniques and create evolving, clever solutions to solve your problems Key Features Master supervised, unsupervised, and semi-supervised ML algorithms and their implementation Build deep learning models for object detection, image classification, similarity learning, and more Build, deploy, and scale end-to-end deep neural network models An alternative, robust approach is based on the usage of quantiles. The left plot has been obtained using logistic regression, while the right plot was obtained with an SVM algorithm with a sixth-degree polynomial kernel. Together with this, other elements, such as the limits for the variance of an estimator, have again attracted the limelight because the algorithms are becoming more and more powerful, and performances that once were considered far from feasible are now a reality. The reader interested in a complete mathematical proof can read High Dimensional Spaces, Deep Learning and Adversarial Examples, Dube S., arXiv:1801.00634 [cs.CV]. To conclude this section, it's useful to consider a general empirical rule derived from the Occam's razor principle: whenever a simpler model can explain a phenomenon with enough accuracy, it doesn't make sense to increase its capacity. Therefore, if we minimize the cross-entropy, we also minimize the Kullback-Leibler divergence, forcing the model to reproduce a distribution that is very similar to pdata. However, if it's possible to obtain unbiased estimators, it's almost impossible to reduce the variance under a well-defined threshold (see the later section related to the Cramér-Rao bound). To solve the problem, we need to find a matrix A, such that: Using the eigendecomposition previously computed, we get: One of the main advantages of whitening is the decorrelation of the dataset, which allows for an easier separation of the components. In this way, those less-varied features lose the ability to influence the end solution (for example, this problem is a common limiting factor when it comes to regressions and neural networks). Therefore, given a dataset and a model, there's always a limit to the ability to generalize. You will be introduced to the most widely used algorithms in supervised, unsupervised, and semi-supervised machine learning, and will … If underfitting was the consequence of a low capacity and a high bias, overfitting is a phenomenon that a high variance can detect. Unfortunately, it's not the best choice for deep learning models, where the datasets are very large and the training processes can take even days to complete. We can now analyze other approaches to scaling that we might choose for specific tasks (for example, datasets with outliers). This is often a secondary problem. The main drawback of this method is its computational complexity. Even if the problem is very hard, we could try to adopt a linear model and, at the end of the training process, the slope and the intercept of the separating line are about -1 and 0 (as shown in the plot); however, if we measure the accuracy, we discover that it's close to 0! This is a very elegant explanation as to why the cross-entropy cost function is an excellent choice for classification problems. Therefore, the Fisher information tends to become smaller, because there are more and more parameter sets that yield similar probabilities; this, at the end of the day, leads to higher variances and an increased risk of overfitting. Because of that, we need to limit our analysis to a sample containing N elements. In fact, we have assumed that X is made up of i.i.d samples, but often two subsequent samples have a strong correlation, which reduces the training performance. His main interests include machine/deep learning, reinforcement learning, big data, and bio-inspired adaptive systems. Considering the previous diagram, generally, we have: The sample is a subset of the potential complete population, which is partially inaccessible. In an ideal scenario, the accuracy should be very similar in all iterations; but in most real cases, the accuracy is quite below average. From the theory, we know that some model families are unbiased (for example, linear regressions optimized using the ordinary least square), but confirming a model is unbiased is extremely different to test when the model is very complex. The cross-validation technique is a powerful tool that is particularly useful when the performance cost is not too high. The shape of the likelihood can vary substantially, from well-defined, peaked curves, to almost flat surfaces. values from pdata, we can create a finite dataset X made up of k-dimensional real vectors: In a supervised scenario, we also need the corresponding labels (with t output values): When the output has more than two classes, there are different possible strategies to manage the problem. If you are looking for a single resource to study, implement, and solve end-to-end machine learning problems and use-cases, this is the book you need. For example, let's imagine that the previous diagram defines four semantically different concepts, which are located in the four quadrants. We can immediately understand that, in the first case, the maximum likelihood can be easily reached by gradient ascent, because the surface is very peaked. Send-to-Kindle or Email . In the third part, we introduced the loss and cost functions, first as proxies of the expected risk, and then we detailed some common situations that can be experienced during an optimization problem. Now, if we rewrite the divergence, we get: The first term is the entropy of the data-generating distribution, and it doesn't depend on the model parameters, while the second one is the cross-entropy. Therefore, it's usually necessary to split the initial set X, together with Y, each of them containing N i.i.d. In some cases, it's also useful to re-shuffle the training set after each training epoch; however, in the majority of our examples, we'll work with the same shuffled dataset throughout the whole process. 61, 10/2018), the success of modern machine learning is mainly due to the ability of deep neural networks to reproduce specific cognitive functions (for example, vision or speech recognition). That's usually because the final goal is to have a reliable set of i.i.d. Fortunately, the introduction of Multilayer Perceptrons (MLP), with non-linear functions, allowed us to overcome this problem, and many other problems whose complexity is beyond the possibilities of any classic machine learning model. Build strong foundation of machine learning algorithms In 7 days. In the following diagram, we see a schematic representation of the Ridge regularization in a bidimensional scenario: The zero-centered circle represents the Ridge boundary, while the shaded surface is the original cost function. In the first part, we have introduced the data generating process, as a generalization of a finite dataset. In classical machine learning, one of the most common approaches is One-vs-All, which is based on training N different binary classifiers, where each label is evaluated against all the remaining ones. In three dimensions, it's easier to understand why a saddle point has been called in this way. Sra S., Nowozin S., Wright S. J. In the previous diagram, the model has been represented by a pseudo-function that depends on a set of parameters defined by the vector θ. In this chapter, we're going to introduce and discuss some fundamental elements. In the following graph, we see the plot of a 15-fold cross-validation performed on a logistic regression: The values oscillate from 0.84 to 0.95, with an average (solid horizontal line) of 0.91.

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