_id stringlengths 2 6 | text stringlengths 3 395 | title stringclasses 1 value |
|---|---|---|
C1100 | A hierarchical clustering is a set of nested clusters that are arranged as a tree. K Means clustering is found to work well when the structure of the clusters is hyper spherical (like circle in 2D, sphere in 3D). Hierarchical clustering don't work as well as, k means when the shape of the clusters is hyper spherical. | |
C1101 | The mean means average. To find it, add together all of your values and divide by the number of addends. The median is the middle number of your data set when in order from least to greatest. The mode is the number that occurred the most often. | |
C1102 | STEPS IN DESIGNING AND CONDUCTING AN RCTGathering the Research Team. Determining the Research Question. Defining Inclusion and Exclusion Criteria. Randomization. Determining and Delivering the Intervention. Selecting the Control. Determining and Measuring Outcomes. Blinding Participants and Investigators.More items | |
C1103 | A scale of 1 : 100 000 means that the real distance is 100 000 times the length of 1 unit on the map or drawing.Example 14. Write the scale 1 cm to 1 m in ratio form. Example 15. Simplify the scale 5 mm : 1 m. Example 16. Simplify the scale 5 cm : 2 km. Example 17. A particular map shows a scale of 1 : 5000. Example 18. | |
C1104 | Classification accuracy is the ratio of correct predictions to total predictions made. classification accuracy = correct predictions / total predictions. 1. classification accuracy = correct predictions / total predictions. It is often presented as a percentage by multiplying the result by 100. | |
C1105 | BFS stands for Breadth First Search. DFS stands for Depth First Search. 2. BFS(Breadth First Search) uses Queue data structure for finding the shortest path. DFS(Depth First Search) uses Stack data structure. | |
C1106 | A normal distribution is determined by two parameters the mean and the variance. Now the standard normal distribution is a specific distribution with mean 0 and variance 1. This is the distribution that is used to construct tables of the normal distribution. | |
C1107 | A convolution is the simple application of a filter to an input that results in an activation. Repeated application of the same filter to an input results in a map of activations called a feature map, indicating the locations and strength of a detected feature in an input, such as an image. | |
C1108 | A curve that represents the cumulative frequency distribution of grouped data on a graph is called a Cumulative Frequency Curve or an Ogive. | |
C1109 | In an analogy to standard deviation, taking the square root of MSE yields the root-mean-square error or root-mean-square deviation (RMSE or RMSD), which has the same units as the quantity being estimated; for an unbiased estimator, the RMSE is the square root of the variance, known as the standard error. | |
C1110 | The outcomes of a random experiment are called events connected with the experiment. For example; 'head' and 'tail' are the outcomes of the random experiment of throwing a coin and hence are events connected with it. Now we can distinguish between two types of events. | |
C1111 | In order to conduct a one-sample proportion z-test, the following conditions should be met: The data are a simple random sample from the population of interest. The population is at least 10 times as large as the sample. n⋅p≥10 and n⋅(1−p)≥10 , where n is the sample size and p is the true population proportion. | |
C1112 | An experimental group is a test sample or the group that receives an experimental procedure. This group is exposed to changes in the independent variable being tested. A control group is a group separated from the rest of the experiment such that the independent variable being tested cannot influence the results. | |
C1113 | A common pattern is the bell-shaped curve known as the "normal distribution." In a normal or "typical" distribution, points are as likely to occur on one side of the average as on the other. Note that other distributions look similar to the normal distribution. | |
C1114 | The law of large numbers, in probability and statistics, states that as a sample size grows, its mean gets closer to the average of the whole population. In the 16th century, mathematician Gerolama Cardano recognized the Law of Large Numbers but never proved it. | |
C1115 | Accuracy is used when the True Positives and True negatives are more important while F1-score is used when the False Negatives and False Positives are crucial. Accuracy can be used when the class distribution is similar while F1-score is a better metric when there are imbalanced classes as in the above case. | |
C1116 | The computational complexity of most metric MDS methods is over O(N2), so that it is difficult to process a data set of a large number of genes N, such as in the case of whole genome microarray data. | |
C1117 | Reinforcement learning (RL) is a significant area of machine learning, with the potential to solve a lot of real world problems in various fields, like game theory, control theory, operations research, information theory, simulation-based optimization, multi-agent systems, swarm intelligence, and statistics. | |
C1118 | The SMD is preferable when the studies in a meta-analysis measure a given outcome using different scales or instruments. | |
C1119 | Some common types of problems built on top of classification and regression include recommendation and time series prediction respectively. Some popular examples of supervised machine learning algorithms are: Linear regression for regression problems. Random forest for classification and regression problems. | |
C1120 | Logarithms are a way of showing how big a number is in terms of how many times you have to multiply a certain number (called the base) to get it. The most common numbers to use are 2, 10, and 2.71828). Logarithms are useful because they are the way our brain naturally understands most things. | |
C1121 | A contingency table, sometimes called a two-way frequency table, is a tabular mechanism with at least two rows and two columns used in statistics to present categorical data in terms of frequency counts. | |
C1122 | The null hypothesis is a general statement that states that there is no relationship between two phenomenons under consideration or that there is no association between two groups. An alternative hypothesis is a statement that describes that there is a relationship between two selected variables in a study. | |
C1123 | The main use of F-distribution is to test whether two independent samples have been drawn for the normal populations with the same variance, or if two independent estimates of the population variance are homogeneous or not, since it is often desirable to compare two variances rather than two averages. | |
C1124 | It tells the algorithm how much you care about misclassified points. SVMs, in general, seek to find the maximum-margin hyperplane. That is, the line that has as much room on both sides as possible. | |
C1125 | Robust statistics are statistics with good performance for data drawn from a wide range of probability distributions, especially for distributions that are not normal. Robust statistical methods have been developed for many common problems, such as estimating location, scale, and regression parameters. | |
C1126 | all sampling units have a logical and have numerical identifier. the sampling frame has some additional information about the units that allow the use of more advanced sampling frames. every element of the population of interest is present in the frame. every element of the population is present only once in the frame. | |
C1127 | On a far grander scale, AI is poised to have a major effect on sustainability, climate change and environmental issues. Ideally and partly through the use of sophisticated sensors, cities will become less congested, less polluted and generally more livable. Inroads are already being made. | |
C1128 | Standard interpretation of the ordered logit coefficient is that for a one unit increase in the predictor, the response variable level is expected to change by its respective regression coefficient in the ordered log-odds scale while the other variables in the model are held constant. | |
C1129 | A partial correlation is basically the correlation between two variables when a third variable is held constant. If we look at the relationship between exercise and weight loss, we see a negative correlation, which sounds bad but isn't. It means that the more I exercise, the more weight I lose. | |
C1130 | Non-linearity is needed in activation functions because its aim in a neural network is to produce a nonlinear decision boundary via non-linear combinations of the weight and inputs. | |
C1131 | Autocorrelation measures the relationship between a variable's current value and its past values. An autocorrelation of +1 represents a perfect positive correlation, while an autocorrelation of negative 1 represents a perfect negative correlation. | |
C1132 | Similarly, an absolute minimum occurs at the x value where the function is the smallest, while a local minimum occurs at an x value if the function is smaller there than points around it (i.e. an open interval around it). | |
C1133 | The proportion of Y variance explained by the linear relationship between X and Y = r2 = 0.64, or 64%. | |
C1134 | Now we'll check out the proven way to improve the performance(Speed and Accuracy both) of neural network models:Increase hidden Layers. Change Activation function. Change Activation function in Output layer. Increase number of neurons. Weight initialization. More data. Normalizing/Scaling data.More items• | |
C1135 | Linear Shift-Invariant systems, called LSI systems for short, form a very important class of practical systems, and hence are of interest to us. They are also referred to as Linear Time-Invariant systems, in case the independent variable for the input and output signals is time. | |
C1136 | The fuzzy K-nearest neighbor algorithm finds memberships of data instances into classes rather than assigning the whole class label. It is beneficial for unlabeled query instance as it is known prior that how much its neighbors belong to a class to improve the accuracy. | |
C1137 | low-dimensional linear mapping of the original high-dimensional data that preserves some feature of interest in the data. Accordingly, linear dimensionality reduction can be used for visualizing or exploring structure in data, denoising or compressing data, extracting meaningful feature spaces, and more. | |
C1138 | The intuition for entropy is that it is the average number of bits required to represent or transmit an event drawn from the probability distribution for the random variable. … the Shannon entropy of a distribution is the expected amount of information in an event drawn from that distribution. | |
C1139 | 1 Introduction. The partial least squares (PLS) algorithm was first introduced for regression tasks and then evolved into a classification method that is well known as PLS-discriminant analysis (PLS-DA). | |
C1140 | A tensor field has a tensor corresponding to each point space. An example is the stress on a material, such as a construction beam in a bridge. Other examples of tensors include the strain tensor, the conductivity tensor, and the inertia tensor. | |
C1141 | Two pixels, p and q, are connected if there is a path from p to q of pixels with property V. A path is an ordered sequence of pixels such that any two adjacent pixels in the sequence are neighbors. An example of an image with a connected component is shown at the right. | |
C1142 | General reporting recommendations such as that of APA Manual apply. One should report exact p-value and an effect size along with its confidence interval. In the case of likelihood ratio test one should report the test's p-value and how much more likely the data is under model A than under model B. | |
C1143 | A quantum bit, more commonly called a qubit, is the basic unit of quantum computing. It can have a value of one or zero or anything in between—at the same time. There are other things qubits can do, but multiple, simultaneous values makes quantum computers faster. | |
C1144 | Softmax regression (or multinomial logistic regression) is a generalization of logistic regression to the case where we want to handle multiple classes. In logistic regression we assumed that the labels were binary: y(i)∈{0,1} . We used such a classifier to distinguish between two kinds of hand-written digits. | |
C1145 | Deep learning networks can be successfully applied to big data for knowledge discovery, knowledge application, and knowledge-based prediction. In other words, deep learning can be a powerful engine for producing actionable results. | |
C1146 | We can say that, when we move from RNN to LSTM, we are introducing more & more controlling knobs, which control the flow and mixing of Inputs as per trained Weights. And thus, bringing in more flexibility in controlling the outputs. So, LSTM gives us the most Control-ability and thus, Better Results. | |
C1147 | In biostatistics, logistic regression is often used when the outcome variable is dichotomous. You also list "SPSS" as a topic. A Google search on <SPSS logistic regression example> will no doubt yield many hits, including the UCLA "textbook examples". | |
C1148 | Density is a measure of mass per volume. The average density of an object equals its total mass divided by its total volume. An object made from a comparatively dense material (such as iron) will have less volume than an object of equal mass made from some less dense substance (such as water). | |
C1149 | Statistical learning theory is a framework for machine learning drawing from the fields of statistics and functional analysis. Statistical learning theory deals with the problem of finding a predictive function based on data. | |
C1150 | When you multiply a matrix by a number, you multiply every element in the matrix by the same number. This operation produces a new matrix, which is called a scalar multiple. For example, if x is 5, and the matrix A is: A = | |
C1151 | As the sample sizes increase, the variability of each sampling distribution decreases so that they become increasingly more leptokurtic. The range of the sampling distribution is smaller than the range of the original population. | |
C1152 | Delta learning does this using the difference between a target activation and an actual obtained activation. Using a linear activation function, network connections are adjusted. Another way to explain the Delta rule is that it uses an error function to perform gradient descent learning. | |
C1153 | The three different types of outliersType 1: Global Outliers (also called “Point Anomalies”)Type 2: Contextual (Conditional) Outliers.Type 3: Collective Outliers.Think of it this way. | |
C1154 | Answer: Autoecncoders work best for image data. | |
C1155 | Therefore, we can reduce the complexity of a neural network to reduce overfitting in one of two ways:Change network complexity by changing the network structure (number of weights).Change network complexity by changing the network parameters (values of weights). | |
C1156 | 8 Methods to Boost the Accuracy of a ModelAdd more data. Having more data is always a good idea. Treat missing and Outlier values. Feature Engineering. Feature Selection. Multiple algorithms. Algorithm Tuning. Ensemble methods. | |
C1157 | These feature maps obtained from convolutional layers are used for classification , but using all the extracted features is computationally expensive. So a Pooling layer is not necessary but advisable to reduce the number of extracted features and to avoid overfitting. | |
C1158 | To measure the relationship between numeric variable and categorical variable with > 2 levels you should use eta correlation (square root of the R2 of the multifactorial regression). If the categorical variable has 2 levels, point-biserial correlation is used (equivalent to the Pearson correlation). | |
C1159 | AdvantagesCost Effective. As the task of assignment ogf random number to different items of population is over, the process is half done. Involves lesser degree of judgment. Comparatively easier way of sampling. Less time consuming. Can be done even by non- technical persons. Sample representative of population. | |
C1160 | It's a method of evaluating how well specific algorithm models the given data. If predictions deviates too much from actual results, loss function would cough up a very large number. Gradually, with the help of some optimization function, loss function learns to reduce the error in prediction. | |
C1161 | One hot encoding is a process by which categorical variables are converted into a form that could be provided to ML algorithms to do a better job in prediction. | |
C1162 | LSTM stands for long short term memory. It is a model or architecture that extends the memory of recurrent neural networks. Typically, recurrent neural networks have 'short term memory' in that they use persistent previous information to be used in the current neural network. | |
C1163 | Etymologically speaking, it's my understanding that kernel is a modernization of cyrnel (Old English, meaning seed ; it's also the word that corn "stems" from, if you'll forgive the pun). A kernel in that context is something from which the rest grows. | |
C1164 | Learning how to use machine learning isn't any harder than learning any other set of libraries for a programmer. The key is to focus on USING it, not designing the algorithm. If you're a programmer and it's incredibly hard to learn ML, you're probably trying to learn the wrong things about it. | |
C1165 | In data science, association rules are used to find correlations and co-occurrences between data sets. They are ideally used to explain patterns in data from seemingly independent information repositories, such as relational databases and transactional databases. | |
C1166 | Business Uses The K-means clustering algorithm is used to find groups which have not been explicitly labeled in the data. This can be used to confirm business assumptions about what types of groups exist or to identify unknown groups in complex data sets. | |
C1167 | Demographic parity or statistical parity suggests that a predictor is unbiased if the prediction ^y is independent of the protected attribute p so that. Pr(^y|p)=Pr(^y). (2.1) Here, the same proportion of each population are classified as positive. | |
C1168 | A Markov network or MRF is similar to a Bayesian network in its representation of dependencies; the differences being that Bayesian networks are directed and acyclic , whereas Markov networks are undirected and may be cyclic. The underlying graph of a Markov random field may be finite or infinite. | |
C1169 | q is the probability of failure 1 - p. | |
C1170 | PF expresses the ratio of true power used in a circuit to the apparent power delivered to the circuit. A 96% power factor demonstrates more efficiency than a 75% power factor. PF below 95% is considered inefficient in many regions. | |
C1171 | Replaces an image by the norm of its gradient, as estimated by discrete filters. The Raw filter of the detail panel designates two filters that correspond to the two components of the gradient in the principal directions. | |
C1172 | Gamma cdf. The gamma distribution is a two-parameter family of curves. The parameters a and b are shape and scale, respectively. p = F ( x | a , b ) = 1 b a Γ ( a ) ∫ 0 x t a − 1 e − t b d t . | |
C1173 | Visualping is the newest, easiest and most convenient tool to monitor websites changes. Our Chrome app allows to monitor pages with only 1 click directly from the page you wish to monitor. Users receive an email when changes are detected but can also set up a Slack integration for team notifications. | |
C1174 | The group of functions that are minimized are called “loss functions”. A loss function is a measure of how good a prediction model does in terms of being able to predict the expected outcome. A most commonly used method of finding the minimum point of function is “gradient descent”. | |
C1175 | How to Calculate a Confusion MatrixYou need a test dataset or a validation dataset with expected outcome values.Make a prediction for each row in your test dataset.From the expected outcomes and predictions count: The number of correct predictions for each class. | |
C1176 | Let's get right into the steps to use Twitter data for sentiment analysis of events:Get Twitter API Credentials: Setup the API Credentials in Python: Getting Tweet Data via Streaming API: Get Sentiment Information: Plot Sentiment Information: Set this up on AWS or Google Cloud Platform: | |
C1177 | The Poisson distribution has the following characteristics: It is a discrete distribution. Each occurrence is independent of the other occurrences. It describes discrete occurrences over an interval. The occurrences in each interval can range from zero to infinity. | |
C1178 | Whereas most machine learning based object categorization algorithms require training on hundreds or thousands of samples/images and very large datasets, one-shot learning aims to learn information about object categories from one, or only a few, training samples/images. | |
C1179 | The primary use of interpolation is to help users, be they scientists, photographers, engineers or mathematicians, determine what data might exist outside of their collected data. Outside the domain of mathematics, interpolation is frequently used to scale images and to convert the sampling rate of digital signals. | |
C1180 | The Cramér-Rao Inequality provides a lower bound for the variance of an unbiased estimator of a parameter. It allows us to conclude that an unbiased estimator is a minimum variance unbiased estimator for a parameter. | |
C1181 | The Cox proportional-hazards model (Cox, 1972) is essentially a regression model commonly used statistical in medical research for investigating the association between the survival time of patients and one or more predictor variables. | |
C1182 | An autoregressive integrated moving average, or ARIMA, is a statistical analysis model that uses time series data to either better understand the data set or to predict future trends. | |
C1183 | Quantile regression forests (QRF) is an extension of random forests developed by Nicolai Meinshausen that provides non-parametric estimates of the median predicted value as well as prediction quantiles. It therefore allows spatially explicit non-parametric estimates of model uncertainty. | |
C1184 | In natural language processing, perplexity is a way of evaluating language models. A language model is a probability distribution over entire sentences or texts. | |
C1185 | KNN algorithm is one of the simplest classification algorithm and it is one of the most used learning algorithms. KNN is a non-parametric, lazy learning algorithm. Its purpose is to use a database in which the data points are separated into several classes to predict the classification of a new sample point. | |
C1186 | Standard Deviation: The Difference. The standard deviation (SD) measures the amount of variability, or dispersion, from the individual data values to the mean, while the standard error of the mean (SEM) measures how far the sample mean of the data is likely to be from the true population mean. | |
C1187 | The next big thing after deep learning Artificial General Intelligence (AGI) that is building machines that can surpass human intelligence. The next big thing after deep learning Artificial General Intelligence (AGI) that is building machines that can surpass human intelligence. | |
C1188 | In SWedge, the Gamma distribution can be useful for any variable which is always positive, such as cohesion or shear strength for example. The Gamma distribution has the following probability density function: where G(a) is the Gamma function, and the parameters a and b are both positive, i.e. a > 0 and b > 0. | |
C1189 | Mutual information is calculated between two variables and measures the reduction in uncertainty for one variable given a known value of the other variable. The mutual information between two random variables X and Y can be stated formally as follows: I(X ; Y) = H(X) – H(X | Y) | |
C1190 | A good example of the advantages of Bayesian statistics is the comparison of two data sets. Classical statistical procedures are F-test for testing the equality of variances and t test for testing the equality of means of two groups of outcomes. | |
C1191 | Analytics helps you form hypotheses, while statistics lets you test them. Statisticians help you test whether it's sensible to behave as though the phenomenon an analyst found in the current dataset also applies beyond it. | |
C1192 | Multiclass classification: classification task with more than two classes. Each sample can only be labelled as one class. For example, classification using features extracted from a set of images of fruit, where each image may either be of an orange, an apple, or a pear. | |
C1193 | A correlation matrix is a table showing correlation coefficients between variables. Each cell in the table shows the correlation between two variables. A correlation matrix is used to summarize data, as an input into a more advanced analysis, and as a diagnostic for advanced analyses. | |
C1194 | Class limits specify the span of data values that fall within a class. Class boundaries are values halfway between the upper class limit of one class and the lower class limit of the next. | |
C1195 | This type of index is called an inverted index, namely because it is an inversion of the forward index. In some search engines the index includes additional information such as frequency of the terms, e.g. how often a term occurs in each document, or the position of the term in each document. | |
C1196 | False positive rate (FPR) is a measure of accuracy for a test: be it a medical diagnostic test, a machine learning model, or something else. In technical terms, the false positive rate is defined as the probability of falsely rejecting the null hypothesis. | |
C1197 | Because when you are constructing a linear regression model you are assuming that your dependent variable "Y" is normally distributed. But when you have a binary dependent variable, this assumption is heavily violated. Thus, it doesn't makes sense to use linear regression when your dependent variable is binary. | |
C1198 | Anchor boxes are a set of predefined bounding boxes of a certain height and width. These boxes are defined to capture the scale and aspect ratio of specific object classes you want to detect and are typically chosen based on object sizes in your training datasets. | |
C1199 | Statistics Definitions > A random walk is a sequence of discrete, fixed-length steps in random directions. Random walks may be 1-dimensional, 2-dimensional, or n-dimensional for any n. A random walk can also be confined to a lattice. |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.