X1, Y1 = make_classification(n_samples=1000,n_features=2, n_redundant=0, n_informative=1, n_clusters_per_class=1) plt.scatter(X1[:,0], X1[:,1], marker='o', c=Y1) plt.subplot(422) plt.title("Two informative features, one cluster per class", fontsize='small') X1, Y1 = mak...
#造伪数据 from sklearn.datasets import make_classification data, target = make_classification(n_samples=100, n_features=2, n_classes=2, n_informative=1, n_redundant=0, n_repeated=0, n_clusters_per_class=1, class_sep=.5,random_state=21) #训练查看效果 tree = DecisionTreeClassifier(max...
分类(Classification) from sklearn import SomeClassifier from sklearn.linear_model import SomeClassifier from sklearn.ensemble import SomeClassifier 1. 2. 3. 回归(Regression) from sklearn import SomeRegressor from sklearn.linear_model import SomeRegressor ...
(max_trials=1) classification_node.set_training( enable_stack_ensemble=False, enable_vote_ensemble=False) command_func = command( inputs=dict( automl_output=Input(type="mlflow_model") ), command="ls ${{inputs.automl_output}}", environment="AzureML-sklearn-0.24-ubuntu18.04-py37-cpu:latest...
Well, the classification rate increased to 77.05%, which is better accuracy than the previous model. Visualizing Decision Trees Let's make our decision tree a little easier to understand using the following code: from six import StringIO from IPython.display import Image from sklearn.tree import...
Note For a tutorial that uses SDK v1 to build a pipeline, see Tutorial: Build an Azure Machine Learning pipeline for image classificationThe core of a machine learning pipeline is to split a complete machine learning task into a multistep workflow. Each step is a manageable component that ...
# Now we check the accuracy of classification # For that, compare the result with test_labels and check which are wrong matches = result==test_labels correct = np.count_nonzero(matches) accuracy = correct*100.0/result.size print accuracy ...
Classification Problem: Traditional MLP code: Link Hybrid code (Mealpy + MLP): Link Mealpy + Neural Network (Optimize Neural Network Hyper-parameter) Code: Link Other Applications Solving Knapsack Problem (Discrete problems): Link Solving Product Planning Problem (Discrete problems): Link Optim...
从sklearn.model_selection导入make_classification从sklearn.linear_model导入train_test_split从 sklearn.metrics导入LogisticRegression导入roc_curve、roc_auc_score导入matplotlib.pyplot作为plt # 生成随机二分类数据集X, y = make_classification(n_samples=1000, n_features=10, n_classes=2, random_...
在make_classification中默认值为True。 shift:一个浮点数或一个长度为n_features的浮点数组或者None。表示将特征值通过某个值进行平移,不然生成的特征值就分布在0点的周围了。在make_classification中默认值为0.0。 scale:一个浮点数或一个长度为n_features的浮点数组或者None。表示将特征值与某个值相乘后的结果赋值...