Machine Learning in Python Essential Techniques for Predictive Analysis by Michael Bowles (z-lib.org).epub

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Math requirements include some undergraduate-level differentialcalculus (knowing how to take a derivative and a little bit of linearalgebra), matrix notation, matrix multiplication, and matrix inverse.The main use of these will be to follow the derivations of some of thealgorithms covered. Many times, that will be as simple as taking aderivative of a simple function or doing some basic matrixmanipulations. Being able to follow the calculations at a conceptuallevel may aid your understanding of the algorithm. Understanding thesteps in the derivation can help you to understand the strengths andweaknesses of an algorithm and can help you to decide whichalgorithm is likely to be the best choice for a particular problem.This book also uses some general probability and statistics. Therequirements for these include some familiarity with undergraduatelevelprobability and concepts such as the mean value of a list of realnumbers, variance, and correlation. You can always look through thecode if some of the concepts are rusty for you.This book covers two broad classes of machine learning algorithms:penalized linear regression (for example, Ridge and Lasso) andensemble methods (for example, Random Forests and GradientBoosting). Each of these families contains variants that will solveregression and classification problems. (You learn the distinctionbetween classification and regression early in the book.)Readers who are already familiar with machine learning and are onlyinterested in picking up one or the other of these can skip to the twochapters covering that family. Each method gets two chapters—onecovering principles of operation and the other running through usageon different types of problems. Penalized linear regression is coveredin Chapter 4, “Penalized Linear Regression,” and Chapter 5,“Building Predictive Models Using Penalized Linear Methods.”Ensemble methods are covered in Chapter 6, “Ensemble Methods,”and Chapter 7, “Building Predictive Models with Python.” Tofamiliarize yourself with the problems addressed in the chapters onusage of the algorithms, you might find it helpful to skim Chapter 2,“Understand the Problem by Understanding the Data,” which dealswith data exploration. Readers who are just starting out with machinelearning and want to go through from start to finish might want to
save Chapter 2 until they start looking at the solutions to problems inlater chapters.
Page 3 and 4: IntroductionExtracting actionable i
Page 5: Who This Book Is ForThis book is in
Page 9 and 10: evolved from overcoming the limitat
Page 11 and 12: ConventionsTo help you get the most
Page 13 and 14: CHAPTER 1The Two Essential Algorith
Page 15 and 16: the flight path, and so on. Having
Page 17 and 18: become an issue. In some problems,
Page 19 and 20: Figure 1.2 Fitting lines with only
Page 21 and 22: Figure 1.3 Binary decision tree exa
Page 23 and 24: For example, if the training set is
Page 25 and 26: NOTE The first pass can usually be
Page 27 and 28: convergence divergence), and RSI (r
Page 29 and 30: runs through code for the core algo
Page 31 and 32: CHAPTER 2Understand the Problem byU
Page 33 and 34: A unique identifier may or may not
Page 35 and 36: The attributes shown in Table 2.1 c
Page 37 and 38: THINGS TO NOTICE ABOUT YOUR NEW DAT
Page 39 and 40: PHYSICAL CHARACTERISTICS OF THE ROC
Page 41 and 42: facilitate iterating through the pr
Page 43 and 44: colCounts.append(type)type = [0]*3s
Page 45 and 46: LISTING 2-3: SUMMARY STATISTICS FOR
Page 47 and 48: catCount = [0]*2for elt in colData:
Page 49 and 50: from the line. That means that the
Page 51 and 52: Figure 2.1 Quantile-quantile plot o
Page 53 and 54: makes it possible to read data into
Page 55 and 56: LISTING 2-5: USING PYTHON PANDAS TO
Page 57 and 58:
Pandas makes it possible to automat
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Figure 2.3 Parallel coordinates gra
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of representative pairs of attribut
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Figure 2.5 Cross- plot of rocks ver
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Figure 2.7 Target-attribute cross-p
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# and add some ditherif rocksVMines
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Equation 2-2: Average values of the
Page 71 and 72:
(sqrt(var2*var3) * numElt)corr221 +
Page 73 and 74:
Figure 2.8 Heat map showing attribu
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that you saw in “Classification P
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plot.ylabel(("Quartile Ranges"))sho
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As an alternative to the listing of
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Figure 2.10 Box plot of real-valued
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work for the abalone problem. Rocks
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for i in range(nrows):#plot rows of
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Figure 2.13 Graph of the logit func
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LISTING 2-12: CORRELATIONCALCULATIO
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Figure 2.15 Correlation heat map fo
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LISTING 2-13: WINE DATA SUMMARY—W
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mean 0.087467 15.87492246.467792 0.
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Figure 2.17 Parallel coordinate plo
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#Try again with normalized valuesfo
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Figure 2.19 Correlation heat map fo
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LISTING 2-15: SUMMARY OF GLASS DATA
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75% 0.610000 9.172500 0.000000 0.10
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Figure 2.21 Parallel coordinate plo
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plot.ylabel(("Attribute Values"))pl
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building predictive models. The too
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The variable that you are attemptin
Page 115 and 116:
Referring to the data set in Table
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ASSESSING PERFORMANCE OF PREDICTIVE
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training on the remaining data. Sta
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Figure 3.2 A complicated classifica
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CONTRAST BETWEEN A SIMPLE MODEL AND
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Figure 3.5 Linear model fit to comp
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Figure 3.7 Linear model fit to smal
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used for predictive modeling. Addin
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Model to Balance Problem Complexity
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LISTING 3-1: COMPARISON OF MSE, MAE
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from the mean) and the standard dev
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Figure 3.9 Confusion matrix example
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LISTING 3-2: MEASURING PERFORMANCEF
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print("Shape of yTrain array", yTra
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examples after it has been deployed
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associated with its removal. If the
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Figure 3.10 In-sample ROC for rocks
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demonstration that performance esti
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preserve the statistical peculiarit
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vector Y contains the labels. And t
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Initialize: Out_of_sample_error = N
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indices = range(len(xList))xListTes
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#scatter plot of actual versus pred
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LISTING 3-4: FORWARD STEPWISEREGRES
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Figure 3.15 Histogram of wine taste
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regression throttle back ordinary r
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xTrain = numpy.array(xListTrain); y
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Figure 3.16 Wine quality prediction
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Figure 3.18 Histogram of wine taste
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LISTING 3-7: ROCKS VERSUS MINES USI
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Listing 3-8 shows the AUC and assoc
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Figure 3.19 AUC for the rocks-versu
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different problem types (regression
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penalty. This chapter explains how
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model for evaluation speed. The num
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In this table, the outcomes are rea
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Equation 4-4: Linear relation betwe
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real numbers—the ones included in
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between zero and the vector space p
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Figure 4.2 Optimum solutions with s
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the sum of squares penalties (the c
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refinement to the forward stepwise
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LISTING 4-1: LARS ALGORITHM FORPRED
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#calculate correlation between attr
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numeric values that are fixed by th
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Figure 4.3 Coefficient curves for L
Page 207 and 208:
LISTING 4-2: 10-FOLD CROSS-VALIDATI
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#Define test and training index set
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looping nxval times. In this case n
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predictably on new data. The more c
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ElasticNet problem given by Equatio
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Initializing and Iterating the Glmn
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#calculate means and variancesxMean
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value of betalabelHat = sum([xNorma
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Figure 4.7 Coefficient curves for r
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This section has gone through two s
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LISTING 4-4: CONVERTING ACLASSIFICA
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#number of steps to takenSteps = 35
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Some problems require deciding amon
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LISTING 4-5: BASIS EXPANSION FOR WI
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squared, logarithmic, and sinusoida
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numRow = [float(row[i]) for i inran
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print(nameList)for i in range(ncols
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classification problem to an ordina
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CHAPTER 5Building Predictive Models
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to use the cross-validation version
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Another way to think about how to p
Page 249 and 250:
LISTING 5-1: USING CROSS-VALIDATION
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#Call LassoCV from sklearn.linear_m
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Figure 5.2 Out-of-sample error with
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make a material difference in the r
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#calculate means and variancesxMean
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#different orderingabsCoef = [abs(a
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Changing Y to un-normalized changes
Page 263 and 264:
LISTING 5-3: USING OUT-OF-SAMPLEERR
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#Convert list of list to np array f
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Figure 5.6 Cross-validation error c
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other. Listing 5-4 initializes an e
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tendency to be overfit. It’s more
Page 273 and 274:
Figure 5.9 Receiver operating chara
Page 275 and 276:
You can accomplish this by replicat
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attrRow = [float(elt) for elt in ro
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elif (predList[irow] >= 0.0) and (y
Page 281 and 282:
TP = tpr[52] * P#FN = False negativ
Page 283 and 284:
LISTING 5-5: COEFFICIENT TRAJECTORI
Page 285 and 286:
alphas, coefs, _ = enet_path(X, Y,l
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0.038531154796719078, 0.00355153481
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Figure 5.10 plots the coefficient c
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the coefficients problematic becaus
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xList.append(row)#separate labels f
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#begin iterationnSteps = 100lamMult
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sumBeta = sum([abs(betaInner[n]) fo
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class. Ones that are large negative
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LISTING 5-7: MULTICLASSCLASSIFICATI
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ySD.append(stdDev)yNormalized = []f
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groups, one plane will do it. If yo
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the prediction. Then that is compar
Page 309 and 310:
CHAPTER 6Ensemble MethodsEnsemble m
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LISTING 6-1: BUILDING A DECISION TR
Page 313 and 314:
HOW A BINARY DECISION TREE GENERATE
Page 315 and 316:
LISTING 6-2: TRAINING A DECISION TR
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lhSse = sum([(s - lhAvg) * (s - lhA
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Figure 6.3 Block diagram of depth 1
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Figure 6.5 shows how the sum square
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Figure 6.6 Prediction using depth 2
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Figure 6.8 Prediction using depth 6
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LISTING 6-3: CROSS-VALIDATION AT AR
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Figure 6.9 Out-of-sample error vers
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different figures of merit than reg
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plot is similar to the plot of the
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#maximum number of models to genera
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Figure 6.11 MSE versus number of tr
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Figure 6.13 shows the curve of MSE
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LISTING 6-5: PREDICTING WINE QUALIT
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mse = []allPredictions = []for iMod
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solitary attributes and therefore c
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demonstrate its variance reduction
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#maximum number of models to genera
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With gradient boosting, tree depth
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Figure 6.19 Gradient boosting predi
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LISTING 6-7: GRADIENT BOOSTING FORP
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#build cumulative prediction from f
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only needing tree depth when there
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idxTest = random.sample(range(nrows
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plot.plot(nModels,mse)plot.axis('ti
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1. 1. Panda Biswanath, Joshua S. He
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given in Chapter 6 helps you unders
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the parameter being ignored, which
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makes it possible to assign differe
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Figure 7.1 Wine taste prediction pe
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xTrain, xTest, yTrain, yTest = trai
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Figure 7.2 Relative importance of v
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ls Least mean squared error.lad Lea
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If the type of max_features is floa
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4. Once the oos performance curve i
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test_size=0.30,random_state=531)# T
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might want to try them both to make
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LISTING 7-3: BUILDING A REGRESSIONM
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latestPrediction = modelList[-1].pr
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Figure 7.5 Wine taste error for Bag
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Predictive Models Using Penalized L
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#list of names forabaloneNames = nu
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ASSESSING PERFORMANCE AND THEIMPORT
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LISTING 7-5: PREDICTING ABALONE AGE
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#plot training and test errors vs n
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Figure 7.9 Abalone age prediction e
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Figure 7.11 Abalone age prediction
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outcomes might be “clicked on the
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classification the labels are 0 or
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LISTING 7-7: CLASSIFYING SONARRETUR
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# Plot feature importancefeatureImp
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# ('Threshold Value = ', 0.46564102
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Figure 7.14 Variable importance for
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This function predicts class probab
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else:labels.append(0)attrRow = [flo
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ctClass = [i*0.01 for i in range(10
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# ('Threshold Value = ', 2.02817801
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Figure 7.16 AUC versus ensemble siz
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Figure 7.18 Mine detection ROC curv
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Figure 7.20 Variable importance for
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Solving Multiclass ClassificationPr
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ncols = len(xNum[1])#Labels are int
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featureImportance = featureImportan
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Figure 7.23 is a bar chart showing
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nrows = len(xNum)ncols = len(xNum[1
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print("Best Missclassification Erro
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As before, the Gradient Boosting ve
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Figure 7.25 Glass classifier built
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Figure 7.27 Glass classifier built
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Table 7.1 Performance and Training
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The chapter demonstrated the use of
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®Machine Learning in Python : Esse
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To my children, Scott, Seth, and Ca
Page 472 and 473:
About the Technical EditorDaniel Po
Page 474 and 475:
IndexerJohnna VanHoose DinseCover D
Page 476:
WILEY END USER LICENSEAGREEMENTGo t
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