python machine learning cookbook chris albon

AboutDTC =Decisiontreeclassifier () $ #Training - Dtc.fit (X_train, Y_train) - #Predicting saved results -Y_predict =dtc.predict (x_test) A + " " the 4 Model Evaluation - " " $ Print("accuracy:", Dtc.score (X_test, y_test)) the Print("Other indicators: \ n", Classification_report (Y_predict, Y_test, target_names=['died','survived'])) the " " the accuracy: 0.7811550151975684 the Other indicators: - Precision recall F1-score support in the died 0.91 0.78 0.84 236 the survived 0.58 0.80 0.67 Abo

", Classification_report (Gbc_y_predict, Y_test, target_names=['died','survived']))103 104 " " the Single decision tree accuracy: 0.7811550151975684106 Other indicators:107 Precision recall F1-score support108 109 died 0.91 0.78 0.84 236 the survived 0.58 0.80 0.67111 the avg/total 0.81 0.78 0.79 329113 the Random forest accuracy: 0.78419452887538 the Other indicators: the Precision recall F1-score support117 118 died 0.91 0.78 0.84 237119 survived 0.58 0.80 0.68 - 121 avg/total 0.82 0.78 0.79

-za-z]"," ", Sent.lower (). Strip ()). Split () in sentences.append (temp) - to returnsentences + - #The sentences in the long news are stripped out for training . thesentences = [] * forIinchx: $Sentence_list =news_to_sentences (i)Panax NotoginsengSentences + =sentence_list - the + #Configure the dimension of the word vector ANum_features = 300 the #the frequency of the words that are to be considered +Min_word_count = 20 - #number of CPU cores used in parallel computing $Num_workers =

#岭回归主要是弥补在数据中出现异常值时, improve the stability of linear model, that is, robustness robustImport Pandas as PDImport NumPy as NPImport Matplotlib.pyplot as PltFrom Sklearn import Linear_modelImport Sklearn.metrics as SM#直接拿最小二乘法数据Ridgerg=linear_model. Ridge (alpha=0.5,fit_intercept=true,max_iter=10000) #alpha nearer to 0, the more the ridge regression approached the linear regression.Ridgerg.fit (X_train,y_train) #训练模型Y_train_pred=ridgerg.predict (X_train) #模型y值Y_test_pred=ridgerg.predict (x_test) #模

Python Kmeans clustering is relatively simple, first requires the import NumPy, from the Sklearn.cluster import Kmeans module:Import NumPy as NP from Import KmeansThen read the TXT file, get the corresponding data and convert it to numpy array:X == open ('rktj4.txt') for in f: = Re.compile ('\s+') x.append ([Float (Regex.Split (v) [3]), float ( Regex.Split (v) [6= Np.array (X)Set the number of classes and cluster:N_clusters = 5= Kmeans (n_clust

Maximumlikelihood (p=w): H,t=defineparam () f1=factorial (h+t)/(factorial (H) *factorial (T)) f2= (p**h) * ((1.0-p) **t) return F1*F2 def factorial (x): return reduce (lambda x,y:x*y,range (1,x+1)) achieve the effect, corresponding to the above example, when h=49,t=31, is the probability of P=2/3 probabilitiesCode Address: Please click on my/********************************* This article from the blog "Bo Li Garvin"* Reprint Please indicate the sourc

It is mentioned in this series that using Python to start machine learning (3: Data fitting and generalized linear regression) mentions the regression algorithm for numerical prediction. The logical regression algorithm is essentially regression, but it introduces a logical function to help classify it. The practice found that the logical regression in the field

, or K nearest neighbor (Knn,k-nearestneighbor) classification algorithm, is one of the simplest methods in data mining classification technology. The so-called K nearest neighbor is the meaning of K's closest neighbour, saying that each sample can be represented by its nearest K-neighbor.The core idea of the KNN algorithm is that if the majority of the k nearest samples in a feature space belong to a category, the sample also falls into this category and has the characteristics of the sample on

!accuracy:87.07%******************* SVM ********************Training took3831. 564000s!accuracy:94.35%******************* GBDT ********************In this data set, because the cluster of data distribution is better (if you understand this database, see its T-sne map can be seen.) Since the task is simple, it has been considered a toy dataset in the deep learning boundary, so KNN has a good effect. GBDT is a very good algorithm, in Kaggle and other bi

Python3 Learning using the APIA sample of a data structure of a dictionary type, extracting features and converting them into vector formSOURCE Git:https://github.com/linyi0604/machinelearningCode:1 fromSklearn.feature_extractionImportDictvectorizer2 3 " "4 dictionary feature Extractor:5 pumping and vectorization of dictionary data Structures6 category type features vectorization with 0 12 values using prototype feature names7 numeric type features r

()--------------------------------------------------------------------------------------------------------------- ---------------------------------------At lastCode SummaryImport NumPy as Npimport cv2from matplotlib import pyplot as PltX = Np.random.randint (25,50, (25,2)) Y = Np.random.randint (6 0,85, (25,2)) Z = Np.vstack ((x, y)) # Convert to np.float32z = Np.float32 (Z) plt.hist (z,100,[0,100]), Plt.show () # define Criteria and apply Kmeans () criteria = (CV2. Term_criteria_eps + CV2. Ter

Print "Performing greedy feature selection ..." score_hist = []n = 10good_features = Set ([]) # greedy Feature selection LOOPW Hile Len (score_hist) if f not in good_features: feats = List (good_features) + [f] Xt = Sparse.hstack ([xts[j] for J in feats]). TOCSR () C5/>score = Cv_loop (Xt, y, model, N) Scores.append ((score, F)) print "Feature:%i Mean AUC:%f"% (f, score) g Ood_features.add (sorted (scores) [ -1][1]) Score_hist.append (sorted

=true) # drop useless columns and create LABELSIDX = test.id.values.astype (int) test = Test.drop ([' id ', ' tube_assembly_id ', ' quote_date '), Axis = 1) labels = Train.cost.valuestrain = Train.drop ([' Quote_date ' , ' cost ', ' tube_assembly_id '], Axis = 1) # Convert data to NumPy Arraytrain = Np.array (train) test = Np.array (test)From:kaggle Copyright NOTICE: This article for Bo Master original article, without Bo Master permission not reproduced. Ma