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

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One way to reveal this sort of mismatch is to divide a set of numbersinto percentiles. For example, the 25th percentile contains thesmallest 25 percent of the data. The 50th percentile contains thesmallest 50 percent of the data. The easiest way to visualize formingthese groupings is to imagine that the data are sorted into numericorder. The numbers in the preceding list are arranged in numericorder. That makes it easy to see where the percentile boundaries go.Some often used percentiles are given special names. The percentilesdefined by dividing the set into equal quarters, fifths, and tenths arecalled respectively quartiles, quintiles, and deciles.With the preceding list, it’s easy to define the quartiles because thelist is ordered and there are eight elements in the list. The firstquartile contains 0.1 and 0.15 and so on. Notice how wide thesequartiles are. The first quartile has a range of 0.5 (0.15–0.1). Thesecond quartile is roughly the same. However, the last quartile has arange of 4.6, which is 100 times larger than the range of the otherquartiles.You can see similar behavior in the quartile boundaries that arecalculated in Listing 2-3. First the program calculates the quartiles.That shows that the upper quartile is much wider than the others. Tobe more certain, the decile boundaries are also calculated andsimilarly demonstrate that the upper decile is unusually wide. Somewidening is normal because distributions often thin out in the tails.VISUALIZATION OF OUTLIERS USINGQUANTILE-QUANTILE PLOTOne way to study outliers in more detail is to plot the distribution ofthe data in question relative to some reasonable distributions to seewhether the relative numbers match up. Listing 2-4 shows how to usethe Python function probplot to help determine whether the data hasoutliers or not. The resulting plot shows how the boundariesassociated with empirical percentiles in the data compare to theboundaries for the same percentiles of a Gaussian distribution. If thedata being analyzed comes from a Gaussian distribution, the pointbeing plotted will lie on a straight line. Figure 2.1 shows that a coupleof points from column 4 of the rocks versus mines data are very far
from the line. That means that the tails of the rocks versus mines datacontain more examples than the tails of a Gaussian density.
Page 3 and 4: IntroductionExtracting actionable i
Page 5 and 6: Who This Book Is ForThis book is in
Page 7 and 8: save Chapter 2 until they start loo
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: catCount = [0]*2for elt in colData:
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
Page 59 and 60: Figure 2.3 Parallel coordinates gra
Page 61 and 62: of representative pairs of attribut
Page 63 and 64: Figure 2.5 Cross- plot of rocks ver
Page 65 and 66: Figure 2.7 Target-attribute cross-p
Page 67 and 68: # and add some ditherif rocksVMines
Page 69 and 70: 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
Page 75 and 76: that you saw in “Classification P
Page 77 and 78: plot.ylabel(("Quartile Ranges"))sho
Page 79 and 80: As an alternative to the listing of
Page 81 and 82: Figure 2.10 Box plot of real-valued
Page 83 and 84: work for the abalone problem. Rocks
Page 85 and 86: for i in range(nrows):#plot rows of
Page 87 and 88: Figure 2.13 Graph of the logit func
Page 89 and 90: LISTING 2-12: CORRELATIONCALCULATIO
Page 91 and 92: Figure 2.15 Correlation heat map fo
Page 93 and 94: LISTING 2-13: WINE DATA SUMMARY—W
Page 95 and 96: mean 0.087467 15.87492246.467792 0.
Page 97 and 98: 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
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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
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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
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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
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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
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Figure 5.9 Receiver operating chara
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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
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TP = tpr[52] * P#FN = False negativ
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LISTING 5-5: COEFFICIENT TRAJECTORI
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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
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CHAPTER 6Ensemble MethodsEnsemble m
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LISTING 6-1: BUILDING A DECISION TR
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HOW A BINARY DECISION TREE GENERATE
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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
Page 470 and 471:
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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