万字阐述-Python构建XGBoost模型

5.1,3.5,1.4,0.2,Iris-setosa4.9,3.0,1.4,0.2,Iris-setosa4.7,3.2,1.3,0.2,Iris-setosa4.6,3.1,1.5,0.2,Iris-setosa5.0,3.6,1.4,0.2,Iris-setosa5.4,3.9,1.7,0.4,Iris-setosa4.6,3.4,1.4,0.3,Iris-setosa5.0,3.4,1.5,0.2,Iris-setosa4.4,2.9,1.4,0.2,Iris-setosa4.9,3.1,1.5,0.1,Iris-setosa5.4,3.7,1.5,0.2,Iris-setosa4.8,3.4,1.6,0.2,Iris-setosa4.8,3.0,1.4,0.1,Iris-setosa4.3,3.0,1.1,0.1,Iris-setosa5.8,4.0,1.2,0.2,Iris-setosa5.7,4.4,1.5,0.4,Iris-setosa5.4,3.9,1.3,0.4,Iris-setosa5.1,3.5,1.4,0.3,Iris-setosa5.7,3.8,1.7,0.3,Iris-setosa5.1,3.8,1.5,0.3,Iris-setosa5.4,3.4,1.7,0.2,Iris-setosa5.1,3.7,1.5,0.4,Iris-setosa4.6,3.6,1.0,0.2,Iris-setosa5.1,3.3,1.7,0.5,Iris-setosa4.8,3.4,1.9,0.2,Iris-setosa5.0,3.0,1.6,0.2,Iris-setosa5.0,3.4,1.6,0.4,Iris-setosa5.2,3.5,1.5,0.2,Iris-setosa5.2,3.4,1.4,0.2,Iris-setosa4.7,3.2,1.6,0.2,Iris-setosa4.8,3.1,1.6,0.2,Iris-setosa5.4,3.4,1.5,0.4,Iris-setosa5.2,4.1,1.5,0.1,Iris-setosa5.5,4.2,1.4,0.2,Iris-setosa4.9,3.1,1.5,0.1,Iris-setosa5.0,3.2,1.2,0.2,Iris-setosa5.5,3.5,1.3,0.2,Iris-setosa4.9,3.1,1.5,0.1,Iris-setosa4.4,3.0,1.3,0.2,Iris-setosa5.1,3.4,1.5,0.2,Iris-setosa5.0,3.5,1.3,0.3,Iris-setosa4.5,2.3,1.3,0.3,Iris-setosa4.4,3.2,1.3,0.2,Iris-setosa5.0,3.5,1.6,0.6,Iris-setosa5.1,3.8,1.9,0.4,Iris-setosa4.8,3.0,1.4,0.3,Iris-setosa5.1,3.8,1.6,0.2,Iris-setosa4.6,3.2,1.4,0.2,Iris-setosa5.3,3.7,1.5,0.2,Iris-setosa5.0,3.3,1.4,0.2,Iris-setosa7.0,3.2,4.7,1.4,Iris-versicolor6.4,3.2,4.5,1.5,Iris-versicolor6.9,3.1,4.9,1.5,Iris-versicolor5.5,2.3,4.0,1.3,Iris-versicolor6.5,2.8,4.6,1.5,Iris-versicolor5.7,2.8,4.5,1.3,Iris-versicolor6.3,3.3,4.7,1.6,Iris-versicolor4.9,2.4,3.3,1.0,Iris-versicolor6.6,2.9,4.6,1.3,Iris-versicolor5.2,2.7,3.9,1.4,Iris-versicolor5.0,2.0,3.5,1.0,Iris-versicolor5.9,3.0,4.2,1.5,Iris-versicolor6.0,2.2,4.0,1.0,Iris-versicolor6.1,2.9,4.7,1.4,Iris-versicolor5.6,2.9,3.6,1.3,Iris-versicolor6.7,3.1,4.4,1.4,Iris-versicolor5.6,3.0,4.5,1.5,Iris-versicolor5.8,2.7,4.1,1.0,Iris-versicolor6.2,2.2,4.5,1.5,Iris-versicolor5.6,2.5,3.9,1.1,Iris-versicolor5.9,3.2,4.8,1.8,Iris-versicolor6.1,2.8,4.0,1.3,Iris-versicolor6.3,2.5,4.9,1.5,Iris-versicolor6.1,2.8,4.7,1.2,Iris-versicolor6.4,2.9,4.3,1.3,Iris-versicolor6.6,3.0,4.4,1.4,Iris-versicolor6.8,2.8,4.8,1.4,Iris-versicolor6.7,3.0,5.0,1.7,Iris-versicolor6.0,2.9,4.5,1.5,Iris-versicolor5.7,2.6,3.5,1.0,Iris-versicolor5.5,2.4,3.8,1.1,Iris-versicolor5.5,2.4,3.7,1.0,Iris-versicolor5.8,2.7,3.9,1.2,Iris-versicolor6.0,2.7,5.1,1.6,Iris-versicolor5.4,3.0,4.5,1.5,Iris-versicolor6.0,3.4,4.5,1.6,Iris-versicolor6.7,3.1,4.7,1.5,Iris-versicolor6.3,2.3,4.4,1.3,Iris-versicolor5.6,3.0,4.1,1.3,Iris-versicolor5.5,2.5,4.0,1.3,Iris-versicolor5.5,2.6,4.4,1.2,Iris-versicolor6.1,3.0,4.6,1.4,Iris-versicolor5.8,2.6,4.0,1.2,Iris-versicolor5.0,2.3,3.3,1.0,Iris-versicolor5.6,2.7,4.2,1.3,Iris-versicolor5.7,3.0,4.2,1.2,Iris-versicolor5.7,2.9,4.2,1.3,Iris-versicolor6.2,2.9,4.3,1.3,Iris-versicolor5.1,2.5,3.0,1.1,Iris-versicolor5.7,2.8,4.1,1.3,Iris-versicolor6.3,3.3,6.0,2.5,Iris-virginica5.8,2.7,5.1,1.9,Iris-virginica7.1,3.0,5.9,2.1,Iris-virginica6.3,2.9,5.6,1.8,Iris-virginica6.5,3.0,5.8,2.2,Iris-virginica7.6,3.0,6.6,2.1,Iris-virginica4.9,2.5,4.5,1.7,Iris-virginica7.3,2.9,6.3,1.8,Iris-virginica6.7,2.5,5.8,1.8,Iris-virginica7.2,3.6,6.1,2.5,Iris-virginica6.5,3.2,5.1,2.0,Iris-virginica6.4,2.7,5.3,1.9,Iris-virginica6.8,3.0,5.5,2.1,Iris-virginica5.7,2.5,5.0,2.0,Iris-virginica5.8,2.8,5.1,2.4,Iris-virginica6.4,3.2,5.3,2.3,Iris-virginica6.5,3.0,5.5,1.8,Iris-virginica7.7,3.8,6.7,2.2,Iris-virginica7.7,2.6,6.9,2.3,Iris-virginica6.0,2.2,5.0,1.5,Iris-virginica6.9,3.2,5.7,2.3,Iris-virginica5.6,2.8,4.9,2.0,Iris-virginica7.7,2.8,6.7,2.0,Iris-virginica6.3,2.7,4.9,1.8,Iris-virginica6.7,3.3,5.7,2.1,Iris-virginica7.2,3.2,6.0,1.8,Iris-virginica6.2,2.8,4.8,1.8,Iris-virginica6.1,3.0,4.9,1.8,Iris-virginica6.4,2.8,5.6,2.1,Iris-virginica7.2,3.0,5.8,1.6,Iris-virginica7.4,2.8,6.1,1.9,Iris-virginica7.9,3.8,6.4,2.0,Iris-virginica6.4,2.8,5.6,2.2,Iris-virginica6.3,2.8,5.1,1.5,Iris-virginica6.1,2.6,5.6,1.4,Iris-virginica7.7,3.0,6.1,2.3,Iris-virginica6.3,3.4,5.6,2.4,Iris-virginica6.4,3.1,5.5,1.8,Iris-virginica6.0,3.0,4.8,1.8,Iris-virginica6.9,3.1,5.4,2.1,Iris-virginica6.7,3.1,5.6,2.4,Iris-virginica6.9,3.1,5.1,2.3,Iris-virginica5.8,2.7,5.1,1.9,Iris-virginica6.8,3.2,5.9,2.3,Iris-virginica6.7,3.3,5.7,2.5,Iris-virginica6.7,3.0,5.2,2.3,Iris-virginica6.3,2.5,5.0,1.9,Iris-virginica6.5,3.0,5.2,2.0,Iris-virginica6.2,3.4,5.4,2.3,Iris-virginica5.9,3.0,5.1,1.8,Iris-virginica

1. Title: Iris Plants Database  Updated Sept 21 by C.Blake - Added discrepency information2. Sources:     (a) Creator: R.A. Fisher     (b) Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)     (c) Date: July, 19883. Past Usage:   - Publications: too many to mention!!!  Here are a few.1. Fisher,R.A. "The use of multiple measurements in taxonomic problems"      Annual Eugenics, 7, Part II, 179-188 (1936); also in"Contributions      to Mathematical Statistics" (John Wiley, NY, 1950).2. Duda,R.O., & Hart,P.E. (1973) Pattern Classification and Scene Analysis.      (Q327.D83) John Wiley & Sons.  ISBN 0-471-22361-1.  See page 218.3. Dasarathy, B.V. (1980) "Nosing Around the Neighborhood: A New System      Structure and Classification Rule for Recognition in Partially Exposed      Environments".  IEEE Transactions on Pattern Analysis and Machine      Intelligence, Vol. PAMI-2, No. 1, 67-71.      -- Results:         -- very low misclassification rates (0% for the setosa class)4. Gates, G.W. (1972) "The Reduced Nearest Neighbor Rule".  IEEE       Transactions on Information Theory, May 1972, 431-433.      -- Results:         -- very low misclassification rates again5. See also:1988 MLC Proceedings, 54-64.  Cheeseman et al's AUTOCLASS II      conceptual clustering system finds 3 classes in the data.4. Relevant Information:   --- This is perhaps the best known database to be found in the pattern       recognition literature.  Fisher's paper is a classic in the fieldand is referenced frequently to this day.  (See Duda & Hart, for       example.)  The data set contains 3 classes of 50 instances each,       where each classreferstoatypeofirisplant.  Oneclassis       linearly separable from the other 2; the latter are NOT linearly       separable from each other.   --- Predicted attribute:classofirisplant.   --- This is an exceedingly simple domain.   --- This data differs from the data presented in Fishers article  (identified by Steve Chadwick,  spchadwick@espeedaz.net )  The 35th sample should be:4.9,3.1,1.5,0.2,"Iris-setosa"  where the error is in the fourth feature.  The 38th sample:4.9,3.6,1.4,0.1,"Iris-setosa"  where the errors are in the second and third features.  5. Number of Instances:150 (50in each of three classes)6. Number of Attributes:4 numeric, predictive attributes and the class7. Attribute Information:1. sepal length in cm2. sepal width in cm3. petal length in cm4. petal width in cm5. class:       -- Iris Setosa      -- Iris Versicolour      -- Iris Virginica8. Missing Attribute Values: NoneSummary Statistics:           Min  Max   Mean    SD   Class Correlation   sepal length:4.37.95.840.830.7826    sepal width:2.04.43.050.43   -0.4194   petal length:1.06.93.761.760.9490  (high!)    petal width:0.12.51.200.760.9565  (high!)9. Class Distribution:33.3% for each of 3 classes.

# encode string class values as integerslabel_encoder = LabelEncoder()label_encoder = label_encoder.fit(Y)label_encoded_y = label_encoder.transform(Y)

# multiclass classificationimport pandasimport xgboostfrom sklearn import model_selectionfrom sklearn.metrics import accuracy_scorefrom sklearn.preprocessing import LabelEncoder# load datadata = pandas.read_csv('iris.csv', header=None)dataset = data.values# split data into X and yX = dataset[:,0:4]Y = dataset[:,4]# encode string class values as integerslabel_encoder = LabelEncoder()label_encoder = label_encoder.fit(Y)label_encoded_y = label_encoder.transform(Y)seed = 7test_size = 0.33X_train, X_test, y_train, y_test = model_selection.train_test_split(X, label_encoded_y, test_size=test_size, random_state=seed)# fit model no training datamodel = xgboost.XGBClassifier()model.fit(X_train, y_train)print(model)# make predictions for test datay_pred = model.predict(X_test)predictions = [round(value) for value in y_pred]# evaluate predictionsaccuracy = accuracy_score(y_test, predictions)print("Accuracy: %.2f%%" % (accuracy * 100.0))

XGBClassifier(base_score=0.5, colsample_bylevel=1, colsample_bytree=1,gamma=0, learning_rate=0.1, max_delta_step=0, max_depth=3,min_child_weight=1, missing=None, n_estimators=100, nthread=-1,objective='multi:softprob', reg_alpha=0, reg_lambda=1,scale_pos_weight=1, seed=0, silent=True, subsample=1)Accuracy: 92.00%

'40-49','premeno','15-19','0-2','yes','3','right','left_up','no','recurrence-events''50-59','ge40','15-19','0-2','no','1','right','central','no','no-recurrence-events''50-59','ge40','35-39','0-2','no','2','left','left_low','no','recurrence-events''40-49','premeno','35-39','0-2','yes','3','right','left_low','yes','no-recurrence-events''40-49','premeno','30-34','3-5','yes','2','left','right_up','no','recurrence-events'...   

# encode string class values as integerslabel_encoder = LabelEncoder()label_encoder = label_encoder.fit(Y)label_encoded_y = label_encoder.transform(Y)

# encode string input values as integersfeatures = []for i in range(0, X.shape[1]):label_encoder = LabelEncoder()feature = label_encoder.fit_transform(X[:,i])features.append(feature)encoded_x = numpy.array(features)encoded_x = encoded_x.reshape(X.shape[0], X.shape[1])

left-upleft-lowright-upright-lowcentral

left-up, left-low, right-up, right-low, central1,0,0,0,00,1,0,0,00,0,1,0,00,0,0,1,00,0,0,0,1

feature = feature.reshape(X.shape[0], 1)

onehot_encoder = OneHotEncoder(sparse=False, categories='auto')feature = onehot_encoder.fit_transform(feature)

# encode string input values as integersencoded_x = Nonefor i in range(0, X.shape[1]):label_encoder = LabelEncoder()feature = label_encoder.fit_transform(X[:,i])feature = feature.reshape(X.shape[0], 1)onehot_encoder = OneHotEncoder(sparse=False, categories='auto')feature = onehot_encoder.fit_transform(feature)if encoded_x is None:encoded_x = featureelse:encoded_x = numpy.concatenate((encoded_x, feature), axis=1)print("X shape: : ", encoded_x.shape)

# binary classification, breast cancer dataset, label and one hot encodedimport numpyfrom pandas import read_csvfrom xgboost import XGBClassifierfrom sklearn.model_selection import train_test_splitfrom sklearn.metrics import accuracy_scorefrom sklearn.preprocessing import LabelEncoderfrom sklearn.preprocessing import OneHotEncoder# load datadata = read_csv('breast-cancer.csv', header=None)dataset = data.values# split data into X and yX = dataset[:,0:9]X = X.astype(str)Y = dataset[:,9]# encode string input values as integersencoded_x = Nonefor i in range(0, X.shape[1]):label_encoder = LabelEncoder()feature = label_encoder.fit_transform(X[:,i])feature = feature.reshape(X.shape[0], 1)onehot_encoder = OneHotEncoder(sparse=False, categories='auto')feature = onehot_encoder.fit_transform(feature)if encoded_x is None:encoded_x = featureelse:encoded_x = numpy.concatenate((encoded_x, feature), axis=1)print("X shape: : ", encoded_x.shape)# encode string class values as integerslabel_encoder = LabelEncoder()label_encoder = label_encoder.fit(Y)label_encoded_y = label_encoder.transform(Y)# split data into train and test setsseed = 7test_size = 0.33X_train, X_test, y_train, y_test = train_test_split(encoded_x, label_encoded_y, test_size=test_size, random_state=seed)# fit model no training datamodel = XGBClassifier()model.fit(X_train, y_train)print(model)# make predictions for test datay_pred = model.predict(X_test)predictions = [round(value) for value in y_pred]# evaluate predictionsaccuracy = accuracy_score(y_test, predictions)print("Accuracy: %.2f%%" % (accuracy * 100.0))

('Xshape: : ', (285, 43))XGBClassifier(base_score=0.5, colsample_bylevel=1, colsample_bytree=1,gamma=0, learning_rate=0.1, max_delta_step=0, max_depth=3,min_child_weight=1, missing=None, n_estimators=100, nthread=-1,objective='binary:logistic', reg_alpha=0, reg_lambda=1,scale_pos_weight=1, seed=0, silent=True, subsample=1)Accuracy: 71.58%

1.TItle: Horse Colic database2.Source Information--Creators: Mary McLeish & Matt CecileDepartmentof Computer ScienceUniversityof GuelphGuelph,Ontario, Canada N1G 2W1mdmcleish@water.waterloo.edu--Donor:    Will Taylor (taylor@pluto.arc.nasa.gov)--Date:     8/6/893.Past Usage:--Unknown4.Relevant Information:--2 data files--horse-colic.data: 300 training instances--horse-colic.test: 68 test instances--Possible class attributes: 24 (whether lesion is surgical)--others include: 23, 25, 26, and 27--Many Data types: (continuous, discrete, and nominal)5.Number of Instances: 368 (300 for training, 68 for testing)6.Number of attributes: 287.Attribute Information:1:  surgery?1 = Yes, it had surgery2 = It was treated without surgery2:  Age1 = Adult horse2 = Young (< 6 months)3:  Hospital Number-numeric id-the case number assigned to the horse(maynot be unique if the horse is treated > 1 time)4:  rectal temperature-linear-in degrees celsius.-An elevated temp may occur due to infection.-temperature may be reduced when the animal is in late shock-normal temp is 37.8-this parameter will usually change as the problem progresseseg.may start out normal, then become elevated because ofthelesion, passing back through the normal range as thehorsegoes into shock5:  pulse-linear-the heart rate in beats per minute-is a reflection of the heart condition: 30 -40 is normal for adults-rare to have a lower than normal rate although athletic horsesmayhave a rate of 20-25-animals with painful lesions or suffering from circulatory shockmayhave an elevated heart rate6:  respiratory rate-linear-normal rate is 8 to 10-usefulness is doubtful due to the great fluctuations7:  temperature of extremities-a subjective indication of peripheral circulation-possible values:1 = Normal2 = Warm3 = Cool4 = Cold-cool to cold extremities indicate possible shock-hot extremities should correlate with an elevated rectal temp.8:  peripheral pulse-subjective-possible values are:1 = normal2 = increased3 = reduced4 = absent-normal or increased p.p. are indicative of adequate circulationwhilereduced or absent indicate poor perfusion9:  mucous membranes-a subjective measurement of colour-possible values are:1 = normal pink2 = bright pink3 = pale pink4 = pale cyanotic5 = bright red / injected6 = dark cyanotic-1 and 2 probably indicate a normal or slightly increasedcirculation-3 may occur in early shock-4 and 6 are indicative of serious circulatory compromise-5 is more indicative of a septicemia10: capillary refill time-a clinical judgement. The longer the refill, the poorer thecirculation-possible values1 = < 3 seconds2 = >= 3 seconds11: pain - a subjective judgement of the horse's pain level-possible values:1 = alert, no pain2 = depressed3 = intermittent mild pain4 = intermittent severe pain5 = continuous severe pain-should NOT be treated as a ordered or discrete variable!-In general, the more painful, the more likely it is to requiresurgery-prior treatment of pain may mask the pain level to some extent12: peristalsis-an indication of the activity in the horse's gut. As the gutbecomesmore distended or the horse becomes more toxic, theactivitydecreases-possible values:1 = hypermotile2 = normal3 = hypomotile4 = absent13: abdominal distension-An IMPORTANT parameter.-possible values1 = none2 = slight3 = moderate4 = severe-an animal with abdominal distension is likely to be painful andhavereduced gut motility.-a horse with severe abdominal distension is likely to requiresurgeryjust tio relieve the pressure14: nasogastric tube-this refers to any gas coming out of the tube-possible values:1 = none2 = slight3 = significant-a large gas cap in the stomach is likely to give the horsediscomfort15: nasogastric reflux-possible values1 = none2 = > 1 liter3 = < 1 liter-the greater amount of reflux, the more likelihood that there issomeserious obstruction to the fluid passage from the rest oftheintestine16: nasogastric reflux PH-linear-scale is from 0 to 14 with 7 being neutral-normal values are in the 3 to 4 range17: rectal examination - feces-possible values1 = normal2 = increased3 = decreased4 = absent-absent feces probably indicates an obstruction18: abdomen-possible values1 = normal2 = other3 = firm feces in the large intestine4 = distended small intestine5 = distended large intestine-3 is probably an obstruction caused by a mechanical impactionandis normally treated medically-4 and 5 indicate a surgical lesion19: packed cell volume-linear-the # of red cells by volume in the blood-normal range is 30 to 50. The level rises as the circulationbecomescompromised or as the animal becomes dehydrated.20: total protein-linear-normal values lie in the 6-7.5 (gms/dL) range-the higher the value the greater the dehydration21: abdominocentesis appearance-a needle is put in the horse's abdomen and fluid is obtained fromtheabdominal cavity-possible values:1 = clear2 = cloudy3 = serosanguinous-normal fluid is clear while cloudy or serosanguinous indicatesacompromised gut22: abdomcentesis total protein-linear-the higher the level of protein the more likely it is to have acompromisedgut. Values are in gms/dL23: outcome-what eventually happened to the horse?-possible values:1 = lived2 = died3 = was euthanized24: surgical lesion?-retrospectively, was the problem (lesion) surgical?-all cases are either operated upon or autopsied so thatthisvalue and the lesion type are always known-possible values:1 = Yes2 = No25,26, 27: type of lesion-first number is site of lesion1 = gastric2 = sm intestine3 = lg colon4 = lg colon and cecum5 = cecum6 = transverse colon7 = retum/descending colon8 = uterus9 = bladder11 = all intestinal sites00 = none-second number is type1 = simple2 = strangulation3 = inflammation4 = other-third number is subtype1 = mechanical2 = paralytic0 = n/a-fourth number is specific code1 = obturation2 = intrinsic3 = extrinsic4 = adynamic5 = volvulus/torsion6 = intussuption7 = thromboembolic8 = hernia9 = lipoma/slenic incarceration10 = displacement0 = n/a28: cp_data-is pathology data present for this case?1 = Yes2 = No-this variable is of no significance since pathology dataisnot included or collected for these cases8.Missing values: 30% of the values are missing

2 1 530101 38.50 66 28 3 3 ? 2 5 4 4 ? ? ? 3 5 45.00 8.40 ? ? 2 2 11300 00000 00000 21 1 534817 39.2 88 20 ? ? 4 1 3 4 2 ? ? ? 4 2 50 85 2 2 3 2 02208 00000 00000 22 1 530334 38.30 40 24 1 1 3 1 3 3 1 ? ? ? 1 1 33.00 6.70 ? ? 1 2 00000 00000 00000 11 9 5290409 39.10 164 84 4 1 6 2 2 4 4 1 2 5.00 3 ? 48.00 7.20 3 5.30 2 1 02208 00000 00000 12 1 530255 37.30 104 35 ? ? 6 2 ? ? ? ? ? ? ? ? 74.00 7.40 ? ? 2 2 04300 00000 00000 2...

dataframe = read_csv("horse-colic.csv", delim_whitespace=True, header=None)

# set missing values to 0X[X == '?'] = 0

# convert to numericX = X.astype('float32')

# encode Y class values as integerslabel_encoder = LabelEncoder()label_encoder = label_encoder.fit(Y)label_encoded_y = label_encoder.transform(Y)

# binary classification, missing datafrom pandas import read_csvfrom xgboost import XGBClassifierfrom sklearn.model_selection import train_test_splitfrom sklearn.metrics import accuracy_scorefrom sklearn.preprocessing import LabelEncoder# load datadataframe = read_csv("horse-colic.csv", delim_whitespace=True, header=None)dataset = dataframe.values# split data into X and yX = dataset[:,0:27]Y = dataset[:,27]# set missing values to 0X[X == '?'] = 0# convert to numericX = X.astype('float32')# encode Y class values as integerslabel_encoder = LabelEncoder()label_encoder = label_encoder.fit(Y)label_encoded_y = label_encoder.transform(Y)# split data into train and test setsseed = 7test_size = 0.33X_train, X_test, y_train, y_test = train_test_split(X, label_encoded_y, test_size=test_size, random_state=seed)# fit model no training datamodel = XGBClassifier()model.fit(X_train, y_train)print(model)# make predictions for test datay_pred = model.predict(X_test)predictions = [round(value) for value in y_pred]# evaluate predictionsaccuracy = accuracy_score(y_test, predictions)print("Accuracy: %.2f%%" % (accuracy * 100.0))

XGBClassifier(base_score=0.5, colsample_bylevel=1, colsample_bytree=1,gamma=0, learning_rate=0.1, max_delta_step=0, max_depth=3,min_child_weight=1, missing=None, n_estimators=100, nthread=-1,objective='binary:logistic', reg_alpha=0, reg_lambda=1,scale_pos_weight=1, seed=0, silent=True, subsample=1)Accuracy: 83.84%

# impute missing values as the meanimputer = SimpleImputer()imputed_x = imputer.fit_transform(X)

# binary classification, missing data, impute with meanimport numpyfrom pandas import read_csvfrom xgboost import XGBClassifierfrom sklearn.model_selection import train_test_splitfrom sklearn.metrics import accuracy_scorefrom sklearn.preprocessing import LabelEncoderfrom sklearn.impute import SimpleImputer# load datadataframe = read_csv("horse-colic.csv", delim_whitespace=True, header=None)dataset = dataframe.values# split data into X and yX = dataset[:,0:27]Y = dataset[:,27]# set missing values to 0X[X == '?'] = numpy.nan# convert to numericX = X.astype('float32')# impute missing values as the meanimputer = SimpleImputer()imputed_x = imputer.fit_transform(X)# encode Y class values as integerslabel_encoder = LabelEncoder()label_encoder = label_encoder.fit(Y)label_encoded_y = label_encoder.transform(Y)# split data into train and test setsseed = 7test_size = 0.33X_train, X_test, y_train, y_test = train_test_split(imputed_x, label_encoded_y, test_size=test_size, random_state=seed)# fit model no training datamodel = XGBClassifier()model.fit(X_train, y_train)print(model)# make predictions for test datay_pred = model.predict(X_test)predictions = [round(value) for value in y_pred]# evaluate predictionsaccuracy = accuracy_score(y_test, predictions)print("Accuracy: %.2f%%" % (accuracy * 100.0))