Evaluate the model: We will evaluate the model's performance on the test set using appropriate metrics for binary classification problems, such as accuracy, precision, recall, and the F1 score. We'll also plot a confusion matrix to visualize the performance. Feature importance: We will look at...
F1指标(F1 measure):以上两个指标的调和平均数 #从sklearn.metrics里导入classification_report模块。fromsklearn.metricsimportclassification_report# 使用逻辑斯蒂回归模型自带的评分函数score获得模型在测试集上的准确性结果。print('Accuracy of LR Classifier:',lr.score(X_test,y_test))# 利用classification_report...
logloss:negative log-likelihood error:Binary classification error rate. It is calculated as #(wrong cases)/#(all cases). For the predictions, the evaluation will regard the instances with prediction value larger than 0.5 as positive instances, and the others...
我使用了这个数据集:https://www.kaggle.com/datasets/rtatman/english-word-frequency 作为起点,通过两个 LLM 查询将 333,333 个 unigram 条目缩减到 120,000 个:“is it a valid English word which is not an acronym or abbreviation?”以及“Is it familiar to a layman?”我试图使用 Llama 来进行词性...
1-(1). Binary Classification with Logistic Regression |Github|Kaggle Step 1. Data Description Step 2. EDA Step 3. Correlation Check Step 4. Test Data Split and Standard Scaling (Test size = 0.3) Step 5. Modeling and Prediction 1-(2). Heart Attack Prediction |Github|Kaggle-by Avinash Bagu...
Stacking allows you to use classifiers for regression problems and vice versa. For instance, one may try a base model with quantile regression(分位数回归) on a binary classification problem. A good stacker should be able to take information from the predictions, even though usually regression is...
Binary Tree Classification Random Forest with Cross Validation XGBoost using TF-IDF to vectorise text (model included) with Cross Validation Deep Learning Models: Pytorch using Bert Based Uncased Model (model not included in github due to large file size) ...
fromtorchmetrics.classificationimport* defevaluate_model(model, test_loader, device): model.to(device) model.eval() accuracy_metric = BinaryAccuracy().to(device) precision_metric = BinaryPrecision().to(device) recall_metric = BinaryRecall().to(device) ...
datasets import make_classification from sklearn.model_selection import train_test_split from kaggler.metrics import auc from kaggler.model import AutoLGB RANDOM_SEED = 42 N_OBS = 10000 N_FEATURE = 100 N_IMP_FEATURE = 20 X, y = make_classification(n_samples=N_OBS, n_features=N_FEATURE...
This awesome list is from:https://www.kaggle.com/code/annavictoria/ml-friendly-public-datasets/notebook 数据集 Binary Classification Indian Liver Patient Records 描述: Patients with Liver disease have been continuously increasing because of excessive consumption of alcohol, inhale of harmful gases, int...