This paper presents a simple computational procedure for generating 'matching' or 'cloning' datasets so that they have exactly the same fitted multiple linear regression equation. The method is simple to implement and provides an alternative to generating datasets under an assumed model. The advantage...
A basic CNN network of two CNN layers, a dense layer and an output layer to recognize the picture of cat or dog. Tensorflow and Keras have been used to implement the layers. The dataset used here is the Kaggle dataset provided by Microsoft. classifier deep-learning jupyter-notebook cnn ...
WEKA is a library of machine learning algorithms to solve data mining problems on real data. WEKA also provides an environment to develop many machine-learning algorithms. It has a set of tools for carrying out various data mining tasks such as data classification, data clustering, regression, a...
Navigate to the mypackage/models/ directory. Create your model class file, ensuring it follows the expected structure and naming conventions. Implement the required methods (get_info, fit, predict) and attributes (topic_dict). Optionally, implement beta, theta, or corresponding methods (get_beta,...
In pursuit of this objective, we implement the random restart strategy, wherein, during each iteration, d sub-tables are randomly selected from the original dataset D, encompassing a randomized subset of rows and columns. To mitigate the risks of overfitting and underfitting inherent in machine ...
imputation algorithm (INS-CLUS-IMPUTE)) [63] to predict missing values. One study [76] implemented a hybrid method Bayesian-Gaussian mixture models (BGMM) for imputing missing values. In this category, 60% of the studies adopted a simulation approach to implement algorithms for data imputation....
from sklearn.linear_model import LogisticRegression lr = LogisticRegression() from sklearn.neighbors import KNeighborsClassifier knn = KNeighborsClassifier() lr.fit(X_train,y_train) knn.fit(X_train,y_train) print("Training Accuracy of KNN: ", knn.score(X_train,y_train)) ...
It is computationally inexpensive and easy to implement19. There are different SD approaches, for example, regression, weather classifications, and weather generators. Regression approaches are very popular, such as multi linear regression (MLR)20, generalized linear model (GLM)21, and machine ...
to implement on a large scale. Furthermore, methods relying on atmospheric profiles become ineffective when the inversion layer appears22,23. Statistical models, such as Spatiotemporal Regression-Kriging16, Geographic Weighted Regression model24, Bayesian Kriging Regression Method25, and Spatially Varying...
In intrusion detection, t-SNE and PCA are often used to implement network traffic visualization. Compared with other preprocessing methods, feature selection and feature extraction are the research points of many articles. Among all the papers we investigated, 38 focus on making improvements to ...