train-test-split

问题 I just noticed that many people tend to use train_test_split even before handling the missing data, and seem like they split the data at the very beginning and there are also a bunch of people, they tend to slipt the data right before model building step after they do all the data cleaning and feature engineering, feature selection stuff. The people tend to split the data at the very first saying that it is to prevent the data leakage. I am right now just so confused about the pipeline of

问题 I just noticed that many people tend to use train_test_split even before handling the missing data, and seem like they split the data at the very beginning and there are also a bunch of people, they tend to slipt the data right before model building step after they do all the data cleaning and feature engineering, feature selection stuff. The people tend to split the data at the very first saying that it is to prevent the data leakage. I am right now just so confused about the pipeline of

问题 I am training an NER model using the python -m spacy train command line tool. I use gold.docs_to_json to convert my annotated documents to the JSON-serializable format. The command line training tool uses both a training set and a development set. I'm not sure how much assistance the command line tools give me for managing train/dev splits. Is there a command line tool to create train/dev splits from a single set of data? Will the spaCy training command do cross-validation for me instead of

问题 I'm trying to split my dataset into a training and a test set by using the train_test_split function from scikit-learn, but I'm getting this error: In [1]: y.iloc[:,0].value_counts() Out[1]: M2 38 M1 35 M4 29 M5 15 M0 15 M3 15 In [2]: xtrain, xtest, ytrain, ytest = train_test_split(X, y, test_size=1/3, random_state=85, stratify=y) Out[2]: Traceback (most recent call last): File "run_ok.py", line 48, in <module> xtrain,xtest,ytrain,ytest = train_test_split(X,y,test_size=1/3,random_state=85

问题 I'm trying to split my dataset into a training and a test set by using the train_test_split function from scikit-learn, but I'm getting this error: In [1]: y.iloc[:,0].value_counts() Out[1]: M2 38 M1 35 M4 29 M5 15 M0 15 M3 15 In [2]: xtrain, xtest, ytrain, ytest = train_test_split(X, y, test_size=1/3, random_state=85, stratify=y) Out[2]: Traceback (most recent call last): File "run_ok.py", line 48, in <module> xtrain,xtest,ytrain,ytest = train_test_split(X,y,test_size=1/3,random_state=85

问题 I am trying to understand the process of model evaluation and validation in machine learning. Specifically, in which order and how the training, validation and test sets must be used. Let's say I have a dataset and I want to use linear regression. I am hesitating among various polynomial degrees (hyper-parameters). In this wikipedia article, it seems to imply that the sequence should be: Split data into training set, validation set and test set Use the training set to fit the model (find the

问题 I am trying to understand the process of model evaluation and validation in machine learning. Specifically, in which order and how the training, validation and test sets must be used. Let's say I have a dataset and I want to use linear regression. I am hesitating among various polynomial degrees (hyper-parameters). In this wikipedia article, it seems to imply that the sequence should be: Split data into training set, validation set and test set Use the training set to fit the model (find the

问题 I'm trying to import the following: from sklearn.model_selection import train_test_split and got following error, here's the stack trace : ImportError Traceback (most recent call last) <ipython-input-1-bdd2a2f20673> in <module> 2 import pandas as pd 3 from sklearn.model_selection import train_test_split ----> 4 from sklearn.tree import DecisionTreeClassifier 5 from sklearn.metrics import accuracy_score 6 from sklearn import tree ~/.local/lib/python3.6/site-packages/sklearn/tree/__init__.py in

问题 Not sure how to fix . Any help much appreciate. I saw thi Vectorization: Not a valid collection but not sure if i understood this train = df1.iloc[:,[4,6]] target =df1.iloc[:,[0]] def train(classifier, X, y): X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=33) classifier.fit(X_train, y_train) print ("Accuracy: %s" % classifier.score(X_test, y_test)) return classifier trial1 = Pipeline([ ('vectorizer', TfidfVectorizer()), ('classifier', MultinomialNB()),

问题 I am following the IRIS example of tensorflow. My case now is I have all data in a single CSV file, not separated, and I want to apply k-fold cross validation on that data. I have data_set = tf.contrib.learn.datasets.base.load_csv(filename="mydata.csv", target_dtype=np.int) How can I perform k-fold cross validation on this dataset with multi-layer neural network as same as IRIS example? 回答1: I know this question is old but in case someone is looking to do something similar, expanding on