A curated set of resources for data science, machine learning, artificial intelligence (AI), data and text analytics, data visualization, big data, and more. Master the craft of predictive modeling by developing strategy, intuition, and a solid foundation in essential concepts In Detail R offers a free and open source. R offers a free and open source environment that is perfect for both learning and deploying Mastering Predictive Analytics with R PDF下载地址( MB).
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introduction to R is also available at ipprofehaphvol.tk ID: www. ipprofehaphvol.tk Jeffrey Strickland. Predictive Analytics using R. Jeffrey. Strickland. This book is intended for the budding data scientist, predictive modeler, or quantitative analyst with only a basic exposure to R and statistics. It is also designed. R offers a free and open source environment that is perfect for both learning and deploying predictive modeling solutions in the real world.
In addition, he has lectured in a number of seminars, specialization programs, and R schools for working data science professionals in Athens. Sign up to our emails for regular updates, bespoke offers, exclusive discounts and great free content.
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Mastering Predictive Analytics with scikit-learn and TensorFlow
What do I get with an eBook? What do I get with a Video? Frequently bought together. Learn more Add to cart. Paperback pages. Book Description R offers a free and open source environment that is perfect for both learning and deploying predictive modeling solutions in the real world. Table of Contents Chapter 1: Gearing Up for Predictive Modeling. Chapter 2: Linear Regression.
Chapter 3: Logistic Regression. Chapter 4: Neural Networks. Chapter 5: Support Vector Machines. Multiclass classification with support vector machines. Chapter 6: Tree-based Methods. Chapter 7: Ensemble Methods. Chapter 8: Probabilistic Graphical Models. Chapter 9: Time Series Analysis. Chapter Topic Modeling. Recommendation Systems. Examples of this include predicting the topic of a website, the next word that will be typed by a user, a person's gender, or whether a patient has a particular disease given a series of symptoms.
The majority of models that we will study in this book fall quite neatly into one of these two categories, although a few, such as neural networks can be adapted to solve both types of problems. It is important to stress here that the distinction made is on the output only, and not on whether the feature values that are used to predict the output are quantitative or qualitative themselves.
In general, features can be encoded in a way that allows both qualitative and quantitative features to be used in regression and classification models alike. Earlier, when we built a kNN model to predict the species of iris based on measurements of flower samples, we were solving a classification problem as our species output variable could take only one of three distinct labels. The kNN approach can also be used in a regression setting; in this case, the model combines the numerical values of the output variable for the selected nearest neighbors by taking the mean or median in order to make its final prediction.
Thus, kNN is also a model that can be used in both regression and classification settings. The term real-time machine learning can refer to two different scenarios, although it certainly does not refer to the idea that real-time machine learning involves making a prediction in real time, that is, within a predefined time limit which is typically small.
For example, once trained, a neural network model can produce its prediction of the output using only a few computations depending on the number of inputs and network layers. This is not, however, what we mean when we talk about real-time machine learning.
A good example of a model that uses real-time machine learning is a weather predictor that uses a stream of incoming readings from various meteorological instruments. Here, the real time aspect of the model refers to the fact that we are taking only a recent window of readings in order to predict the weather.
The further we go back in time, the less relevant the readings will be and we can, thus, choose to use only the latest information in order to make our prediction. Of course, models that are to be used in a real-time setting must also be able to compute their predictions quicklyit is not of much use if it takes hours for a system taking measurements in the morning to compute a prediction for the evening, as by the time the computation ends, the prediction won't be of much value.
When talking about models that take into account information obtained over a recent time frame to make a prediction, we generally refer to models that have been trained on data that is assumed to be representative of all the data for which the model will be asked to make a prediction in the future. A second interpretation of real-time machine learning arises when we describe models that detect that the properties of the process being modeled have shifted in some way.
We will focus on examples of the first kind in this book when we look at time series models. The process of predictive modeling By looking at some of the different characterizations of models, we've already hinted at various steps of the predictive modeling process.
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In this section, we will present these steps in a sequence and make sure we understand how each of these contributes to the overall success of the endeavor. In a predictive analytics project, this question involves drilling into the type of prediction that we want to make and understanding the task in detail.
For example, suppose we are trying to build a model that predicts employee churn for a company. We first need to define this task precisely, while trying to avoid making the problem overly broad or overly specific. We could measure churn as the percentage of new full time hires that defect from the company within their first six months. Notice that once we properly define the problem, we have already made some progress in thinking about what data we will have to work with. For example, we won't have to collect data from part-time contractors or interns.
This task also means that we should collect data from our own company only, but at the same time recognize that our model might not necessarily be applicable to make predictions for the workforce of a different company.
If we are only interested in churn, it also means that we won't need to make predictions about employee performance or sick days although it wouldn't hurt to ask the person for whom we are building the model, to avoid surprises in the future.
Once we have a precise enough idea of the model we want to build, the next logical question to ask is what sort of performance we are interested in achieving, and how we will measure this. That is to say, we need to define a performance metric for our model and then a minimum threshold of acceptable performance. We will go into substantial detail on how to assess the performance of models in this book.
For now, we want to emphasize that, although it is not unusual to talk about assessing the performance of a model after we have trained it on some data, in practice it is important to remember that defining the expectations and performance target for our model is something that a predictive modeler should discuss with the stakeholders of a project at the very beginning.
Models are never perfect and it is easy to spiral into a mode of forever trying to improve performance. Clear performance goals are not only useful in guiding us to decide which methods to use, but also in knowing when our model is good enough.
Finally, we also need to think about the data that will be available to us when the time comes to collect it, and the context in which the model will be used.
For example, suppose we know that our employee churn model will be used as one of the factors that determine whether a new applicant in our company will be hired. In this context, we should only collect data from our existing employees that were available before they were hired. We cannot use the result of their first performance review, as these data won't be available for a prospective applicant.
Collecting the data can often be the most time and resource consuming part of the entire process, which is why it is so critical that the first step of defining the task and identifying the right data to be collected is done properly.
When we learn about how a model, such as logistic regression works we often do this by way of an example data set and this is largely the approach we'll follow in this book. Unfortunately, we don't have a way to simulate the process of collecting the data, and it may seem that most of the effort is spent on training and refining a model.
When learning about models using existing data sets, we should bear in mind that a lot of effort has usually gone into collecting, curating, and preprocessing the data. We will look at data preprocessing more closely in a subsequent section. While we are collecting data, we should always keep in mind whether we are collecting the right kind of data.
Mastering Predictive Analytics with R - Second Edition
Many of the sanity checks that we perform on data during preprocessing also apply during collection, in order for us to spot whether we have made a mistake early on in the process. For example, we should always check that we measure features correctly and in the right units.
We should also make sure that we collect data from sources that are sufficiently recent, reliable, and relevant to the task at hand.
In the employee churn model we described in the previous section, as we collect information about past employees we should ensure that we are consistent in measuring our features. For example, when measuring how many days a person has been working in our company, we should consistently use either calendar days or business days. We should also try to get information from as broad a sample as possible and not introduce a hidden bias in our data collection. For example, if we wanted a general model for employee churn, we would not want to collect data from only female employees or employees from a single department.
How do we know when we have collected enough data? Early on when we are collecting the data and have not built and tested any model, it is impossible to tell how much data we will eventually need, and there aren't any simple rules of thumb that we can follow.
We can, however, anticipate that certain characteristics of our problem will require more data.
For example, when building a classifier that will learn to predict from one of three classes, we may want to check whether we have enough observations representative of each class.
Similarly, for regression models, it is also useful to check that the range of the output variable in the training data corresponds to the range that we would like to predict. If we are building a regression model that covers a large output range, we will also need to collect more data compared to a regression model that covers a smaller output range under the same accuracy requirements.
Another important factor to help us estimate how much data we will need, is the desired model performance. Intuitively, the higher the accuracy that we need for our model, the more data we should collect.
We should also be aware that improving model performance is not a linear process. Getting from 90 to 95 percent accuracy can often require more effort and a lot more data, compared to making the leap from 70 to 90 percent. Models that have fewer parameters or are simpler in their design, such as linear regression models, often tend to need less data than more complex models such as neural networks.
Finally, the greater the number of features that we want to incorporate into our model, the greater the amount of data we should collect. In addition, we should be aware of the fact that this requirement for additional data is also not going to be linear. That is to say, building a model with twice the number of features often requires much more than twice the amount of original data.
This should be readily apparent, if we think of the number of different combinations of inputs our model will be required to handle.Recommendation Systems. Insights Tutorials. Gearing Up for Predictive Modeling. Machine Learning with R - Second Edition.
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Quick links: Sign up here to get these deals straight to your inbox. Log in. This task also means that we should collect data from our own company only, but at the same time recognize that our model might not necessarily be applicable to make predictions for the workforce of a different company. For example, suppose we know that our employee churn model will be used as one of the factors that determine whether a new applicant in our company will be hired.
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