HomeUncategorizedmastering machine learning algorithms packt pdf

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. If the analysis of the dataset has highlighted the presence of outliers and the task is very sensitive to the effect of different variances, robust scaling is the best choice. Considering the previous example, a linear model (for example, a logistic regression) can only modify the slope and the intercept of the separating line. Conversely, two points whose angle is very small can always be considered similar. In scikit-learn, it's possible to split the original dataset using the train_test_split() function, which allows specifying the train/test size, and if we expect to have randomly shuffled sets (which is the default). When working with a set of K parameters, the Fisher information becomes a positive semidefinite matrix: This matrix is symmetric, and also has another important property: when a value is zero, it means that the corresponding couple of parameters are orthogonal for the purpose of the maximum likelihood estimation, and they can be considered separately. The default value for correct is True: As we have previously discussed, the numerosity of the sample available for a project is always limited. Therefore, unless we explicitly declare otherwise, in this book you can always assume we are working with a single data generating process, from which all the samples will be drawn. 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. This isn't a limitation, just a didactic choice: The goal of a learning process is to estimate the parameter so as, for example, to maximize the accuracy of its classifications. He is author of several publications including Machine Learning Algorithms and Hands-On Unsupervised Learning with Python, published by Packt. For example, if we want to split X and Y, with 70% training and 30% test, we can use: Shuffling the sets is always good practice, in order to reduce the correlation between samples (the method train_test_split has a parameter called shuffle that allows this to be done automatically). In fact, we have assumed that X is made up of i.i.d samples, but several times two subsequent samples have a strong correlation, reducing the training performance. The left plot has been obtained using logistic regression, while, for the right one, the algorithm is SVM with a sixth-degree polynomial kernel. You will be introduced to the most widely used algorithms in supervised, unsupervised, and semi-supervised machine learning, and will … You will be introduced to the most widely used algorithms in supervised, unsupervised, and semi-supervised machine learning, and will … As pointed out by Darwiche (in Darwiche A., Human-Level Intelligence or Animal-Like Abilities?, Communications of the ACM, Vol. This is the code repository for Mastering C++ Programming, published by Packt. The challenging goal of machine learning is to find the optimal strategies to train models using a limited amount of information, to find all the necessary abstractions that justify their logical processes. For example, considering the set A = {1, 2, 3, 5, 7, 9}, we have: If we add the value 10, the set A, we get : In a similar way, we can define other percentiles or quantiles. Just as for AUC diagrams, in a binary classifier we consider the threshold of 0.5 as lower bound, because it corresponds to a random choice of the label. This characterization justifies the use of the word approximately in the definition, which could lead to misunderstandings if not fully mathematically defined. To understand the problem, consider the following classification scenarios: Acceptable fitting (left), overfitted classifier (right). In other words, we want to find a set of parameters so that: When the cost function has more than two parameters, it's very difficult and perhaps even impossible to understand its internal structure; however, we can analyze some potential conditions using a bidimensional diagram:  Different kinds of points in a bidimensional scenario. This is brilliant, because once the model has been successfully trained and validated with a positive result, it's reasonable to assume that the output corresponding to never-seen samples reflects the real-world joint probability distribution. It uses Stratified K-Fold for categorical classifications and Standard K-Fold for all other cases. The real power of machine learning resides in its algorithms, which make even the most difficult things capable of being handled by machines. In this chapter, we discussed fundamental concepts shared by almost any machine learning model. Publisher: Packt. Even if this condition is stronger in deep learning contexts, we can think of a model as a gray box (some transparency is guaranteed by the simplicity of many common algorithms), where a vectorial input, Schema of a generic model parameterized with the vector θ. In many simple cases, this is true and can be easily verified; but with more complex datasets, the problem becomes harder. 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. Let's now compute the derivative of the bias with respect to the vector θ (it will be useful later): Consider that the last equation, thanks to the linearity of E[•], holds also if we add a term that doesn't depend on x to the estimation of θ. Moreover, it has the enormous advantage of allowing reuse of the same models for different purposes without the need to re-train them from scratch, which is currently often a necessary condition to achieve acceptable performances. Part of Packt’s Beginners Guide series: ... Mastering Machine Learning with R ... Write clean and elegant Python code to optimize the strength of your machine learning algorithms; Discover how to embed your machine learning model in a web application for increased accessibility; When working with NumPy and scikit-learn, it's always a good practice to set the random seed to a constant value, so as to allow other people to reproduce the experiment with the same initial conditions. We also introduced the Vapnik-Chervonenkis theory, which is a mathematical formalization of the concept of representational capacity, and we analyzed the effects of high biases and high variances. Therefore, we can cut them out from the computation by setting an appropriate quantile. 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. In a machine learning task, our goal is to achieve the maximum accuracy, starting from the training set and then moving on to the validation set. The first one is that there's a scale difference between the real sample covariance and the estimation , often adopted with the Singular Value Decomposition (SVD). The extra capacity could reduce the generalization ability, if. When minimizing g(x), we need to also consider the contribution of the gradient of the norm in the ball centered in the origin where, however, the partial derivatives don't exist. This means that the capacity of the model is high enough or even excessive for the task (the higher the capacity, the higher the probability of large variances), and that the training set isn't a good representation of pdata. They create associations, find out relationships, discover patterns, generate new samples, and more, working with well-defined datasets, which are homogenous collections of data points (for example, observations, images, or measures) related to a specific scenario (for example, the temperature of a room sampled every 5 minutes, or the weights of a population of individuals). This value can be interpreted as the speed of the gradient when the function is reaching the maximum; therefore, higher values imply better approximations, while a hypothetical value of zero means that the probability to determine the right parameter estimation is also null. If the training accuracy is high enough, this means that the capacity is appropriate or even excessive for the problem; however, we haven't considered the role of the likelihood . This effect is related to the fact that the model has probably reached a very high training accuracy through over-learning a limited set of relationships, and it has almost completely lost its ability to generalize (that is, the average validation accuracy decays when never-seen samples are tested). This means that the training set has been built excluding samples that contain features necessary to let the model fit the separating hypersurface considering the real pdata. A machine learning problem is focused on learning abstract relationships that allow a consistent generalization when new samples are provided. Mastering Data Analysis With R Mastering Data Analysis With R by Gergely Daroczi, Mastering Data Analysis With R Books available in PDF, EPUB, Mobi Format. 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. A machine learning model must consider this kind of abstraction as a reference. Shai Shalev-Shwartz is an Associate Professor at the School of Computer Science and Engineering at The Hebrew University, Israel. This book will address the problems related to accurate and efficient data classification and prediction. In this common case, we assume that the transition between concepts is semantically smooth, so two points belonging to different sets can always be compared according to their common features (for example, the boundary between warm and cold can be a point whose temperature is the average between the two groups). The surface is very similar to a horse saddle, and if we project the point on an orthogonal plane, XZ is a minimum, while on another plane (YZ) it is a maximum. These conditions are very strong in logic and probabilistic contexts, where the inferred conditions must reflect natural ones. If we are training a classifier, our goal is to create a model whose distribution is as similar as possible to pdata. However, with the advancement in the technology and requirements of data, machines will have to be smarter than they are today to meet the overwhelming data needs; mastering these algorithms and using them optimally is the need of the hour. Remember that the estimation is a function of X, and cannot be considered a constant in the sum. We need to find the optimal number of folds so that cross-validation guarantees an unbiased measure of the performances. The mathematical proof is beyond the scope of this book; however, it's possible to understand it intuitively by considering the following diagram (bidimensional): The zero-centered square represents the Lasso boundaries. Build strong foundation of machine learning algorithms In 7 days. Let's consider the scenario shown in the following graph: Underfitted classifier: The curve cannot separate correctly the two classes. An easy-to-follow, step-by-step guide for getting to grips with the real-world application of machine learning algorithms, Discover powerful ways to effectively solve real-world machine learning problems using key libraries including scikit-learn, TensorFlow, and PyTorch, Discover a project-based approach to mastering machine learning concepts by applying them to everyday problems using libraries such as scikit-learn, TensorFlow, and Keras, Grasp machine learning concepts, techniques, and algorithms with the help of real-world examples using Python libraries such as TensorFlow and scikit-learn. In this way, N-1 classifications are performed to determine the right class. Let's explore the following plot: XOR problem with different separating curves. Shuffling has to be avoided when working with sequences and models with memory. To understand this concept, it's necessary to introduce an important definition: the Fisher information. Independent of the number of iterations, this model will never be able to learn a good association between X and Y. More formally, the Fisher information quantifies this value. The idea of capacity, for example, is an open-ended question that neuroscientists keep on asking themselves about the human brain. In the first part, we have introduced the data generating process, as a generalization of a finite dataset. You will also discover practical applications for complex techniques such as maximum likelihood estimation, Hebbian learning, and ensemble learning, and how to use TensorFlow 2.x to train effective deep neural networks. As it's possible to see, the standard scaling performs a shift of the mean and adjusts the points so that it's possible to consider them as drawn from N(0, I). 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. Another classical example is the XOR function. Description : Download Mastering Machine Learning Algorithms or read Mastering Machine Learning Algorithms online books in PDF, EPUB and Mobi Format. Considering both the training and test accuracy trends, we can conclude that in this case a training set larger than about 270 points doesn't yield any strong benefit. Independently from the number of iterations, this model will never be able to learn the association between X and Y. In some contexts, such as Natural Language Processing (NLP), two feature vectors are different in proportion to the angle they form, while they are almost insensitive to Euclidean distance. This also implies that, in many cases, if k << Nk, the sample doesn't contain enough of the representative elements that are necessary to rebuild the data generating process, and the estimation of the parameters risks becoming clearly biased. The dataset is represented by data extracted from a real-world scenario, and the outcomes provided by the model must reflect the nature of the actual relationships. When working with a set of K parameters, the Fisher information becomes a positive semidefinite matrix: This matrix is symmetrical, and also has another important property: when a value is zero, it means that the corresponding parameters are orthogonal for the purpose of the maximum likelihood estimation, and they can be considered separately. Even if we think to 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. 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. Now, let's introduce some important data preprocessing concepts that will be helpful in many practical contexts. He received his M.Sc.Eng. A large variance implies dramatic changes in accuracy when new subsets are selected. For example, suppose that an estimated parameter and the true mean is actually 0. In particular, we discussed effects called underfitting and overfitting, defining the relationship with high bias and high variance. If we have a class of sets C and a set M, we say that C shatters M if: In other words, given any subset of M, it can be obtained as the intersection of a particular instance of C (cj) and M itself. Instead, using a polynomial classifier (for example, a parabolic one), the problem can be easily solved. As its value is always quadratic when the distance between the prediction and the actual value (corresponding to an outlier) is large, the relative error is high, and this can lead to an unacceptable correction. That means we can summarize the previous definition, saying that for a PAC learnable problem, . High-capacity models, in particular, with small or low-informative datasets, can drive to flat likelihood surfaces with a higher probability than lower-capacity models. In this case, only 6 samples are used for testing purposes (1.2%), which means the validation is not particularly reliable, and the average value is associated with a very large variance (that is, in some lucky cases, the CV accuracy can be large, while in the remaining ones, it can be close to 0).

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