scikit-learn

问题 This might look like a trivial problem. But I am getting stuck in predicting results from a model. My problem is like this: I have a dataset of shape 1000 x 19 (except target feature) but after one hot encoding it becomes 1000 x 141. Since I trained the model on the data which is of shape 1000 x 141, so I need data of shape 1 x 141 (at least) for prediction. I also know in python, I can make future prediction using model.predict(data) But, since I am getting data from an end user through a

问题 This might look like a trivial problem. But I am getting stuck in predicting results from a model. My problem is like this: I have a dataset of shape 1000 x 19 (except target feature) but after one hot encoding it becomes 1000 x 141. Since I trained the model on the data which is of shape 1000 x 141, so I need data of shape 1 x 141 (at least) for prediction. I also know in python, I can make future prediction using model.predict(data) But, since I am getting data from an end user through a

问题 This might look like a trivial problem. But I am getting stuck in predicting results from a model. My problem is like this: I have a dataset of shape 1000 x 19 (except target feature) but after one hot encoding it becomes 1000 x 141. Since I trained the model on the data which is of shape 1000 x 141, so I need data of shape 1 x 141 (at least) for prediction. I also know in python, I can make future prediction using model.predict(data) But, since I am getting data from an end user through a

问题 I am new to python and machine learning. I have a Linear Regression model which is able to predict output based on the input which I have dumped to be used with a web service. See the code below: X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25) regression_model = LinearRegression() regression_model.fit(X_train, y_train) print(regression_model.predict(np.array([[21, 0, 0, 0, 1, 0, 0, 1, 1, 1]]))) # this is returning my expected output joblib.dump(regression_model, '..

问题 I experimented with breast cancer data from scikit-learn. Use all features and not use standardscaler: cancer = datasets.load_breast_cancer() x = cancer.data y = cancer.target x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=42) pla = Perceptron().fit(x_train, y_train) y_pred = pla.predict(x_test) print(accuracy_score(y_test, y_pred)) result 1 : 0.9473684210526315 Use all features and use standardscaler: cancer = datasets.load_breast_cancer() x = cancer

问题 Can someone explain why does the random_state parameter affects the model so much? I have a RandomForestClassifier model and want to set the random_state (for reproducibility pourpouses), but depending on the value I use I get very different values on my overall evaluation metric (F1 score) For example, I tried to fit the same model with 100 different random_state values and after the training ad testing the smallest F1 was 0.64516129 and the largest 0.808823529). That is a huge difference.

问题 I am applying Decision Tree to a data set, using sklearn In Sklearn there is a parameter to select the depth of the tree - dtree = DecisionTreeClassifier(max_depth=10). My question is how the max_depth parameter helps on the model. how does high/low max_depth help in predicting the test data more accurately? 回答1: max_depth is what the name suggests: The maximum depth that you allow the tree to grow to. The deeper you allow, the more complex your model will become. For training error, it is

问题 I want to prevent certain phrases for creeping into my models. For example, I want to prevent 'red roses' from entering into my analysis. I understand how to add individual stop words as given in Adding words to scikit-learn's CountVectorizer's stop list by doing so: from sklearn.feature_extraction import text additional_stop_words=['red','roses'] However, this also results in other ngrams like 'red tulips' or 'blue roses' not being detected. I am building a TfidfVectorizer as part of my

问题 I want to prevent certain phrases for creeping into my models. For example, I want to prevent 'red roses' from entering into my analysis. I understand how to add individual stop words as given in Adding words to scikit-learn's CountVectorizer's stop list by doing so: from sklearn.feature_extraction import text additional_stop_words=['red','roses'] However, this also results in other ngrams like 'red tulips' or 'blue roses' not being detected. I am building a TfidfVectorizer as part of my

问题 I want to prevent certain phrases for creeping into my models. For example, I want to prevent 'red roses' from entering into my analysis. I understand how to add individual stop words as given in Adding words to scikit-learn's CountVectorizer's stop list by doing so: from sklearn.feature_extraction import text additional_stop_words=['red','roses'] However, this also results in other ngrams like 'red tulips' or 'blue roses' not being detected. I am building a TfidfVectorizer as part of my