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The combination crusher is a new generation high efficiency crushing machine designed and researched by integrating the domestic and foreign crusher technology with the same kinds and optimizing the main technical parameters.

classification dataset

Aug 28, 2020 · There are two main types of classification tasks: binary and multi-class classification, divided based on the number of labels to be predicted for a given dataset as two or more than two respectively. Given the prevalence of classification tasks in machine learning, we will treat these two subtypes of classification problems separately

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  • delve datasets- department of computer science

    delve datasets- department of computer science

    Datasets are categorized as primarily assessment, development or historical according to their recommended use. Within each category we have distinguished datasets as regression or classification according to how their prototasks have been created. Details on how to install the downloaded datasets are given below

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  • top 10image classification datasets for machine learning

    top 10image classification datasets for machine learning

    Dec 18, 2019 · Intel Image Classification – Created by Intel for an image classification contest, this expansive image dataset contains approximately 25,000 images. Furthermore, the images are divided into the following categories: buildings, forest, glacier, mountain, sea, and street

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  • machine learning datasets in r(10datasetsyou can use

    machine learning datasets in r(10datasetsyou can use

    Aug 15, 2020 · You need standard datasets to practice machine learning. In this short post you will discover how you can load standard classification and regression datasets in R. This post will show you 3 R libraries that you can use to load standard datasets and 10 specific datasets that you can use for machine learning in R. It is invaluable to load standard datasets in

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  • machine learning classification: a dataset-based pictorial

    machine learning classification: a dataset-based pictorial

    The concept of classification in machine learning is concerned with building a model that separates data into distinct classes. This model is built by inputting a set of training data for which the classes are pre-labeled in order for the algorithm to learn from. The model is then used by inputting a different dataset for which the classes are withheld, allowing the model to predict their class membership based on what it …

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  • classification datasetsresults - rodrigo benenson

    classification datasetsresults - rodrigo benenson

    The Street View House Numbers (SVHN) Dataset. SVHN is a real-world image dataset for developing machine learning and object recognition algorithms with …

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  • a public repository of data for static-analysis

    a public repository of data for static-analysis

    Nov 02, 2020 · To date, research into the classification of meta-alerts has been constrained by the absence of a complete and reliable dataset of meta-alert data. This blog post describes a new repository of labeled data that CERT is making publicly available for many code-flaw conditions

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  • classificationalgorithms for imbalanceddatasets- blockgeni

    classificationalgorithms for imbalanceddatasets- blockgeni

    Jul 26, 2020 · Identifying outliers in data is referred to as outlier or anomaly detection and a subfield of machine learning focused on this problem is referred to as one-class classification. These are unsupervised learning algorithms that attempt to model “ normal ” examples in order to classify new examples as either normal or abnormal (e.g. outliers)

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  • textclassificationusing transformers | by vatsal saglani

    textclassificationusing transformers | by vatsal saglani

    The dataset we will be using is a question classification dataset. The two sets of categories provide information about what type of answer would be required for a question asked. You can find the dataset here. For example, the question asked is What are liver enzymes? This question requires a descriptive text and most suitably a definition

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  • classificationmetrics - literature review centre

    classificationmetrics - literature review centre

    Is it significantly different than the accuracy of any of the other models?Based on what you know about each classifiers strengths and weaknesses (Section 4.5), what might the different models be indicating about the dataset?Based on what you can tell from exploratory visualization of the dataset, do you see any indications that one model

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  • ag news classification dataset| kaggle

    ag news classification dataset| kaggle

    The AG's news topic classification dataset is constructed by choosing 4 largest classes from the original corpus. Each class contains 30,000 training samples and 1,900 testing samples. The total number of training samples is 120,000 and testing 7,600. The file classes.txt contains a list of classes corresponding to each label

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  • techniques for handling imbalancedclassification datasets

    techniques for handling imbalancedclassification datasets

    Nov 16, 2020 · An imbalanced dataset is one in which one class has disproportionate observations compared to the other classes. Each class in the dataset does not have an equal representation and imbalance causes the skewed class distribution. You have to run a classification algorithm to distinguish between a benign tumor and a cancerous tumor

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  • finding hard samples in your imageclassification dataset

    finding hard samples in your imageclassification dataset

    For this example, we will be using the test split of the image classification dataset, CIFAR-10. This dataset contains 10,000 test images labeled across 10 different classes. This is one of the dozens of datasets in the FiftyOne Dataset Zoo, so we can easily load it up. We can use the FiftyOne App to …

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  • uci machine learning repository:data sets

    uci machine learning repository:data sets

    Heterogeneity Activity Recognition: The Heterogeneity Human Activity Recognition (HHAR) dataset from Smartphones and Smartwatches is a dataset devised to benchmark human activity recognition algorithms (classification, automatic data segmentation, sensor fusion, feature extraction, etc.) in real-world contexts; specifically, the dataset is gathered with a variety of different device models and use …

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  • basic classification: classify images of clothing

    basic classification: classify images of clothing

    Mar 19, 2021 · Fashion MNIST is intended as a drop-in replacement for the classic MNIST dataset—often used as the "Hello, World" of machine learning programs for computer vision. The MNIST dataset contains images of handwritten digits (0, 1, 2, etc.) in a format …

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  • all thedatasetsyou need to practice data science skills

    all thedatasetsyou need to practice data science skills

    Aug 25, 2020 · This dataset contains information on different types of news from BBC archives. It’s a big text dataset. It is normally popular for Multiclass Classification problems. The dataset is big but it has only two columns: text and category. Here is the link for this dataset. Cats vs Dogs. Very commonly used to practice Image Classification

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  • classification datasetsresults - rodrigo benenson

    classification datasetsresults - rodrigo benenson

    The Street View House Numbers (SVHN) Dataset. SVHN is a real-world image dataset for developing machine learning and object recognition algorithms with …

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  • classificationof irisdataset. hi everyone ! | by sriraag

    classificationof irisdataset. hi everyone ! | by sriraag

    Oct 12, 2019 · Classifying dataset using logistic regression. Logistic regression uses Sigmoid function for predicting values. logreg = LogisticRegression() logreg.fit(X_train, y_train)

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