線形回帰は、単純線形回帰とも呼ばれ、従属変数と1つの独立変数の間の関係をモデル化する回帰アルゴリズムです。線形回帰モデルは、線形または傾斜した直線である関係を示すので、単純線形回帰という名前になりました。

Few-shot classification is a challenging problem as only very few training examples are given for each new task. One of the effective research lines to address this challenge focuses on learning deep representations driven by a similarity measure between a query image and few support images of some class. Statistically, this amounts to measure the dependency of image features, viewed as random vectors in a high-dimensional embedding space. Previous methods either only use marginal distributions without considering joint distributions, suffering from limited representation capability, or are computationally expensive though harnessing joint distributions. In this paper, we propose a deep Brownian Distance Covariance (DeepBDC) method for few-shot classification. The central idea of DeepBDC is to learn image representations by measuring the discrepancy between joint characteristic functions of embedded features and product of the marginals. As the BDC metric is decoupled, we formulate it as a highly modular and efficient layer. Furthermore, we instantiate DeepBDC in two different few-shot classification frameworks. We make experiments on six standard few-shot image benchmarks, covering general object recognition, fine-grained categorization and cross-domain classification. Extensive evaluations show our DeepBDC significantly outperforms the counterparts, while establishing new state-of-the-art results. The source code is available at http://www.peihuali.org/DeepBDC

The ability to analyze single cells is one of the holy grails of precision medicine. Yuri Belotti, PhD, Doorgesh Sharma Jokhun, PhD, and Professor Chwee Teck (C.T.) Lim at National University of Singapore have developed a novel protocol for single-cell classification based on intracellular pH. Their paper entitled Machine learning based approach to pH imaging and classification of single cancer cells was published in APL Bioengineering. The pH in the human the body varies between 4.7 and 8.0. Cancer growth, metastasis, and other diseases including Alzheimer's have been linked to deviations from normal intracellular acidity.

In this paper, we present a study of a kernel-based consensual aggregation on randomly projected high-dimensional features of predictions for regression. The aggregation scheme is composed of two steps: the high-dimensional features of predictions, given by a large number of regression estimators, are randomly projected into a smaller subspace using Johnson-Lindenstrauss Lemma in the first step, and a kernel-based consensual aggregation is implemented on the projected features in the second step. We theoretically show that the performance of the aggregation scheme is close to the performance of the aggregation implemented on the original high-dimensional features, with high probability. Moreover, we numerically illustrate that the aggregation scheme upholds its performance on very large and highly correlated features of predictions given by different types of machines. The aggregation scheme allows us to flexibly merge a large number of redundant machines, plainly constructed without model selection or cross-validation. The efficiency of the proposed method is illustrated through several experiments evaluated on different types of synthetic and real datasets.

Absolutely no previous experience required. Absolutely no previous experience required. Are you aiming for a career in Data Science or Data Analytics? Good news, you don't need a Maths degree - this course is equipping you with the practical knowledge needed to master the necessary statistics. It is very important if you want to become a Data Scientist or a Data Analyst to have a good knowledge in statistics & probability theory.

Before we understand more about Logistic Regression, let's first recap some important definitions which will give us a better understanding of the topic. Logistic Regression comes under Supervised Learning. Supervised Learning is when the algorithm learns on a labeled dataset and analyses the training data. These labeled data sets have inputs and expected outputs. Supervised learning can be further split into classification and regression. Classification is about predicting a label, by identifying which category an object belongs to based on different parameters.

During my university studies, I attended a course named Statistical Data Analysis. I was excited about this course because it taught me all the basic statistical analysis methods such as (non-)linear regression, ANOVA, MANOVA, LDA, PCA, etc. However, I never learned about the business application of these methods. During the course, we worked with several examples. Still, all the samples were CSV datasets, mainly from Kaggle.

Originally published on Towards AI the World's Leading AI and Technology News and Media Company. If you are building an AI-related product or service, we invite you to consider becoming an AI sponsor. At Towards AI, we help scale AI and technology startups. Let us help you unleash your technology to the masses. Time series data is one of the most common data types in the industry and you will probably be working with it in your career. Therefore understanding how to work with it and how to apply analytical and forecasting techniques are critical for every aspiring data scientist. In this series of articles, I will go through the basic techniques to work with time-series data, starting from data manipulation, analysis, visualization to understand your data and prepare it for and then using statistical, machine, and deep learning techniques for forecasting and classification. It will be more of a practical guide in which I will be applying each discussed and explained concept to real data.

Qiuchong Chen,1,* Yixue Zhang,1,* Mengjun Zhang,1 Ziying Li,1 Jindong Liu1,2 1Department of Anesthesiology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, People's Republic of China; 2Jiangsu Province Key Laboratory of Anesthesiology, Xuzhou Medical University, Xuzhou, Jiangsu, People's Republic of China *These authors contributed equally to this work Correspondence: Jindong Liu, Department of Anesthesiology, The Affiliated Hospital of Xuzhou Medical University, 99 Huaihai Road West, Quanshan District, Xuzhou, Jiangsu, 221000, People's Republic of China, Email [email protected] Objective: There has been a worldwide increment in acute kidney injury (AKI) incidence among elderly orthopedic operative patients. The AKI prediction model provides patients' early detection a possibility at risk of AKI; most of the AKI prediction models derive, however, from the cardiothoracic operation. The purpose of this study is to predict the risk of AKI in elderly patients after orthopedic surgery based on machine learning algorithm models. Methods: We organized a retrospective study being comprised of 1000 patients with postoperative AKI undergoing orthopedic surgery from September 2016, to June, 2021. They were divided into training (80%;n 799) and test (20%;n 201) sets.We utilized nine machine learning (ML) algorithms and used intraoperative information and preoperative clinical features to acquire models to predict AKI. The performance of the model was evaluated according to the area under the receiver operating characteristic (AUC), sensitivity, specificity and accuracy. Select the optimal model and establish the nomogram to make the prediction model visualization. The concordance statistic (C-statistic) and calibration curve were used to discriminate and calibrate the nomogram respectively. Results: In predicting AKI, nine ML algorithms posted AUC of 0.656– 1.000 in the training cohort, with the randomforest standing out and AUC of 0.674– 0.821 in the test cohort, with the logistic regression model standing out.

For a given predictor, the t-statistic (and its associated p-value) tests whether or not there is a statistically significant relationship between a given predictor and the outcome variable, that is whether or not the beta coefficient of the predictor is significantly different from zero. Null hypothesis (H0): the coefficients are equal to zero (i.e., no relationship between x and y) Alternative Hypothesis (Ha): the coefficients are not equal to zero (i.e., there is some relationship between x and y) Another aspect to pay attention to your linear models is the p-value of the coefficients. A p-value indicates whether or not you can reject or accept a hypothesis. A very small p value means that the predictor is probably an excellent addition to your model. A standard way to test if the predictors are not meaningful is looking if the p-values smaller than 0.05.