sklearn auc

: precision and RECALL,PR-CURVE,AUC, classified against inferior answers (A) vs Classification for high-quality answers (B). A's precision and recall are very low and need not be considered. B The effect is good, further adjust the threshold, you can get 80% precision,recall 37%, can you tolerate a low recall? ---> Classifier slimming: Determine the importance of features by the coefficients of logistic regression and remove unimportant features.

satisfaction, performance evaluation, monthly working time and working life and turnover. number of participating projects and whether or not to leave a stacked bar chart labs (x = ' left ', y = ' promotion_last_5years ') bar_5years percentage of promotions and separations within 5 years stacked bar chart percentage of salary and separations stacked bar chart third, the regression tree of modeling prediction four, modeling prediction of the simple Bayesian v. Model

and movie_target.npy to save time. 3, Code and analysis The code for the logical regression is as follows: #-*-Coding:utf-8-*-from matplotlib import pyplot import scipy as SP import numpy as NP from matplotlib import Pylab F Rom sklearn.datasets import load_files from sklearn.cross_validation import train_test_split from sklearn.feature_ Extraction.text Import Countvectorizer from Sklearn.feature_extraction.text import Tfidfvectorizer from Sklearn.naive_ Bayes import MULTINOMIALNB from sklear

(such as sample size, shape and element composition, etc.) obtained by the molecule descriptor, the descriptor has been normalized.First time CommitThe game is a two-dollar classification problem whose data has been extracted and selected to make preprocessing easier, and although the game is over, you can still submit a solution so you can see comparisons with the world's best data scientists.Here, I use the random forest algorithm to train and predict, although the random forest is a more adv

PIP list under CMD to view the successfully installed package.The notes are displayed on the machine as follows:Cycler (0.10.0)Matplotlib (2.0.0)NumPy (1.13.3+MKL)Pip (9.0.1)Pyparsing (2.2.0)Python-dateutil (2.6.1)Pytz (2017.3)Scikit-learn (0.19.1)SciPy (0.19.0)Setuptools (28.8.0)Six (1.11.0)Alternatively, you can test for successful installation in the following waysEnter the following command under Python, if you do not make an error, the installation is successful, you can learn happily:Impo

Nonsense not much to say directly on the code: Import NumPy as NP from Sklearn import datasets x,y = Datasets.make_classification (n_samples=100,n_features=2, n_redundant=0,n_classes=2,random_state=7816) print (x.shape,y.shape) X = X.astype (np.float32) y = y * 2-1 ' detach data ' from Sklearn import model_selection as Ms X_train, X_test, y_train, y_test = Ms.train_test_split ( X, y, test_size=0.2, ra

principle of pruning is that when the neural network is trained to actively remove the neurons that are useless, the characteristic of the neuron is that the W is very small, so we can get the neural network to look at the weight matrix of W and decide whether to delete some neurons.3. Draw the learning curve.4. If the learning curve is not stable, then reduce the learning rate; if the learning curve changes very slowly, then increase the learning rate.5. If the learning curve has been found to

Macport command:sudo port sync//synchronizes local and global ports tree, but does not check whether it has updates.sudo port install python36//install PYTHON36sudo port install Py36-pipsudo port select--set python python36sudo port select--set pip pip36Pip install--user Sklearn//use PIP to install some Python dependency packagesPip install--user scipy//Matrix Data Processing LibraryPip install--user matplotlib//python in a powerful drawing modulePip

Scikit-learn Getting Started-Xuan Sen2. Installing the SoftwarePython 2.0 I recommend using the "pip install scikit-learn" or "easy_install scikit-learn" fully automatic installation, and then through "From sklearn import feature_extraction" Import .If the error "Unknown encoding:cp65001" appears during installation, enter "Chcp 936" to change the encoding from Utf-8 to Simplified Chinese GBK.two. TF-IDF Basic knowledgeRefer to the official docu

Classification Simple example>>>FromSklearnImportTree>>>X=[[0,0],[1,1]]>>>y = [1]>>> clf = tree. decisiontreeclassifier () >>> clf = clf. fit (xy) >>> clf. ([[2. 2. array ([1]) >>>FromSklearnImportTree>>>X=[[0,0],[2,2]]>>>Y=[0.5,2.5]>>>ClF=tree. () >> > clf = clf. fit (xy) >>> clf. ([[11]) array ([0.5]) /span> The decision tree algorithm uses sample datasets such as:The procedure is as follows:[Python]View Plain copy "FONT-SIZE:18PX;

lazy Learning Algorithm Summary Chapter 4 build a good training set-data preprocessing Process Missing Values Remove features or samples with missing values Rewrite Missing Value Understanding the estimator API in sklearn Process classified data Splits a dataset into a training set and a test set. Unified feature value range Select meaningful features Evaluate feature importance using random Forest Summary

different algorithms, the concept of F1 value is put forward on the basis of precision and recall, and the overall evaluation of precision and recall is made. F1 is defined as follows:F1值 = 正确率 * 召回率 * 2 / (正确率 + 召回率) Python Code implementation It sklearn is easy to implement the above logic. 123456789101112131415161718192021 from sklearn import neighbors, datasets, metricsimport

in this paper, a Python implementation decision tree algorithm is described. Share to everyone for your reference, as follows: From sklearn.feature_extraction import dictvectorizerimport csvfrom sklearn import treefrom sklearn Import Preprocessingfrom sklearn.externals.six Import stringio# reads the CSV data and stores the data and eigenvalues in the Dictionary and class label list allelectronicsdata = op

Python algorithm walkthrough _ One Rule algorithm (detailed description), python_one In this way, a feature has only 0 and 1 values, and the dataset has three categories. If Category A has 20 such individuals, Category B has 60 such individuals, and category C has 20 such individuals. Therefore, when this feature is set to 0, Class B is the most likely. However, there are still 40 individuals not in Class B. Therefore, the error rate of dividing this feature from 0 to Class B is 40%. Then, all f

value in the formula Coef _: parameter vector (w in the formula) Mse_path _: mean square error of each cross Verification Alphas _: alpha value used during verification There are so many theoretical foundations related to this test. Next we will start the experiment: Data Source Import numpy as npimport matplotlib. pyplot as pltfrom sklearn import linear_modelfrom sklearn. linear_model import LassoCVimport

practice and understand some simple principles of clustering classification algorithms, you can write kmeans And Naive Bayes, because these libraries all have third-party libraries. If you do not need a large amount of data, you can directly use the sklearn library, which is especially convenient. If there is a large amount of data to be distributed, I only use mapreduce to write data that is not distributed and there are many ready-made libraries. T

PackageName search: Pip search PackageName Help: Pip Help#查看已经安装的模块: List Installed Packages[Email protected]:~$ pip list[Email protected]:~$ wget https://pypi.python.org/pypi/setuptools[Email protected]:~$ pip--versionPip 1.5.6 from/usr/lib/python2.7/dist-packages (python2.7)#安装setuptools:　　[Email protected]:~$ sudo apt-get install Python-setuptools#查看Ubuntu中安装的Python路径[Email protected]:~$ whereis pythonpython:/usr/bin/python2.7[email protected]:~$ which python/usr/bin/python#安装numpy模块和matplot

法包from sklearn.neighbors Import kneighborsclassifier def knnclassify (Traindata,trainlabel, TestData): knnclf=kneighborsclassifier () #default: K = 5,defined by Yourself:kneighborsclassifier (n_neighbors=10 ) Knnclf.fit (Traindata,ravel (Trainlabel)) testlabel=knnclf.predict (testData) Saveresult (TestLabel, ' Sklearn_knn_result.csv ') return TestLabelKNN algorithm package can set its own parameter k, the default k=5, the above comments is described. More specific use, recommend

Reference: http://scikit-learn.org/stable/modules/model_persistence.htmlafter the model has been trained, we want to be able to save it and use the trained saved model directly when encountering a new sample without having to retrain the model again. This section describes the application of pickle in saving the model. (aftertraining a scikit-learn model, it's desirable to has a-a-persist the model for the Out has to retrain. The following section gives a example of what to persist a model with

successfully:Then wait until the download is complete. As for why download this one can be, the above link has mentioned:In fact, the version should be the latest edition, so, after the use of anaconda and then according to their own needs to install a variety of packages and so on.To GitHub scikit-learn Download Scikit-learn, installation methods and so on the link inside, I will not say more.2. Install it in accordance with other methods on the Internet.This is to go online to check the infor