label="实际数据") predictions.predicted_mean.plot(ax=ax, label="预测数据") ax.fill_between(predictions.conf_int().index, predictions.conf_int().iloc[:, 0], predictions.conf_int().iloc[:, 1], color='b', alpha=0.1) ax.legend() plt.show() ...
np.random.seed(337)sample=arma_sample(n=510,ar=[0.9],ma=[0.0])q=10nma=NeuralMA(q)nma.fit(sample,batch_size=32,nb_epoch=50)score=nma.evaluate()print(score)nma.plot_predictions('fit_ma.png') 浏览完整代码
predict_class = test_model.predict(img) desc = tf.keras.applications.mobilenet_v2.decode_predictions(predict_class, top=3) print(desc[0]) #!/usr/bin/env python3 # 引入所使用到的扩展库 from __future__ import absolute_import, division, print_function import os import numpy as np import ...
Filter Predictions in Python Segment Anything 2 License Step 1 Install Supervision First, install the supervision pip package: pip install supervision Once you have installed supervision, you are ready to load your data and start writing logic to filter detections. ...
Python sklearn paired_distances用法及代码示例 Python sklearn precision_recall_curve用法及代码示例 Python sklearn jaccard_score用法及代码示例 Python sklearn WhiteKernel用法及代码示例 Python sklearn CalibrationDisplay.from_predictions用法及代码示例 Python sklearn VotingRegressor用法及代码示例 Python sklearn ge...
(mat_con,cmap=plt.cm.YlOrRd,alpha=0.5)forminrange(mat_con.shape[0]):forninrange(mat_con.shape[1]):px.text(x=m,y=n,s=mat_con[m,n],va="center",ha="center",size="xx-large")# Sets the labelsplt.xlabel("Predictions",fontsize=16)plt.ylabel("Actuals",fontsize=16)plt.title(...
[1] to just shap_values. I am currently working on a binary classification project using the random forest algorithm. My objective is to explain the model predictions using SHAP values. Additionally, I have a list called "will give a hint" which contains two objects. These objects are most...
metrics.plot_confusion_matrix(y, predictions, normalize=True) plt.show() 图:归一化混淆矩阵 其他图如学习曲线、特征重要性、聚类的肘点等等,都可以用几行代码搞定。 图:学习曲线、特征重要性 图:K-means肘点图 03 总结 本文对Scikit-plot做下简单介绍,这是一个机器学习的画图神器,几行代码就能画出高大...
writer.add_figure('predictions vs. actuals', plot_classes_preds(net, inputs, labels), global_step=epoch * len(trainloader) + i) 1. 2. 3. 看指标 一个“ PR Curves”选项卡,其中包含每个类别的精确调用曲线。继续戳一下;您会看到,在某些类别上,模型的“曲线下面积”接近100%,而在另一些类别上...
Technically this “works” if you transform the Y variable as well, but the resulting plot is misleading, and the prediction interval is for the transformed variable. E.g. if you pass a formula'np.log(TotalCrime+1) ~ TotalLic', you would need to exponentiate the the predictions and subtr...