udacity python machine learning

", 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

Preface: "The foundation determines the height, not the height of the foundation!" The book mainly from the coding program, data structure, mathematical theory, data processing and visualization of several aspects of the theory of machine learning, and then extended to the probability theory, numerical analysis, matrix analysis and other knowledge to guide us into the world of

Learning notes for "Machine Learning Practice": Implementation of k-Nearest Neighbor algorithms, and "Machine Learning Practice" k- The main learning and research tasks of the last semester were pattern recognition, signal theor

1. The complete course of statistics all of statistics Carnegie Kimelon Wosseman 2. Fourth edition, "Probability Theory and Mathematical Statistics" Morris. Heidegger, Morris H.degroot, and Mark. Schevish (Mark j.shervish) 3. Introduction to Linear algebra, Gilbert. Strong--Online video tutorials are classic 4. "Numerical linear algebra", Tracy Füssen. Lloyd and David. Bao Textbooks suitable for undergraduates 5. Predictive data analysis of machine