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C2700 | The standard deviation is simply the square root of the variance. The average deviation, also called the mean absolute deviation , is another measure of variability. However, average deviation utilizes absolute values instead of squares to circumvent the issue of negative differences between data and the mean. | |
C2701 | The Perceptron algorithm learns the weights for the input signals in order to draw a linear decision boundary. This enables you to distinguish between the two linearly separable classes +1 and -1. Note: Supervised Learning is a type of Machine Learning used to learn models from labeled training data. | |
C2702 | Linear discriminant analysis is primarily used here to reduce the number of features to a more manageable number before classification. Each of the new dimensions is a linear combination of pixel values, which form a template. | |
C2703 | In artificial intelligence, an expert system is a computer system that emulates the decision-making ability of a human expert. Expert systems are designed to solve complex problems by reasoning through bodies of knowledge, represented mainly as if–then rules rather than through conventional procedural code. | |
C2704 | Degrees of Freedom refers to the maximum number of logically independent values, which are values that have the freedom to vary, in the data sample. Degrees of Freedom are commonly discussed in relation to various forms of hypothesis testing in statistics, such as a Chi-Square. | |
C2705 | Secondly, there is more than one way to reduce overfitting: Enlarge your data set by using augmentation techniques such as flip, scale, Using regularization techniques like dropout (you already did it), but you can play with dropout rate, try more than or less than 0.5.More items• | |
C2706 | The t-test is a method that determines whether two populations are statistically different from each other, whereas ANOVA determines whether three or more populations are statistically different from each other. | |
C2707 | Yes, decoherence does solve the measurement problem of quantum mechanics. Decoherence explains why, after a measurement, you would get the same result if you immediately made the same measurement again. So the claim being made is that decoherence explains why the wavefunction appears to collapse. | |
C2708 | In regression analysis, you need to standardize the independent variables when your model contains polynomial terms to model curvature or interaction terms. This problem can obscure the statistical significance of model terms, produce imprecise coefficients, and make it more difficult to choose the correct model. | |
C2709 | Appropriate Problems for Decision Tree LearningInstances are represented by attribute-value pairs. The target function has discrete output values. Disjunctive descriptions may be required. The training data may contain errors. The training data may contain missing attribute values. | |
C2710 | In this blog we will learn what is calibration and why and when we should use it. We calibrate our model when the probability estimate of a data point belonging to a class is very important. Calibration is comparison of the actual output and the expected output given by a system. | |
C2711 | One of the ways to help deal with this bias is to avoid shaping participants' ideas or experiences before they are faced with the experimental material. Even stating seemingly innocuous details might prime an individual to form theories or thoughts that could bias their answers or behavior. | |
C2712 | The t-test is a method that determines whether two populations are statistically different from each other, whereas ANOVA determines whether three or more populations are statistically different from each other. | |
C2713 | A rolling hash (also known as recursive hashing or rolling checksum) is a hash function where the input is hashed in a window that moves through the input. At best, rolling hash values are pairwise independent or strongly universal. They cannot be 3-wise independent, for example. | |
C2714 | Each feature, or column, represents a measurable piece of data that can be used for analysis: Name, Age, Sex, Fare, and so on. Features are also sometimes referred to as “variables” or “attributes.” Depending on what you're trying to analyze, the features you include in your dataset can vary widely. | |
C2715 | Predictive validity refers to the degree to which scores on a test or assessment are related to performance on a criterion or gold standard assessment that is administered at some point in the future. | |
C2716 | shutdown point | |
C2717 | value is the split of the samples at each node. so at the root node, 32561 samples are divided into two child nodes of samples each. – | |
C2718 | Social media plays an important role in every student's life. It is often easier and more convenient to access information, provide information and communicate via social media. Tutors and students can be connected to each other and can make good use of these platforms for the benefit of their learning and teaching. | |
C2719 | We have compiled a list of best practices and strategies that you can use to improve your TensorFlow Lite model performance.Choose the best model for the task. Profile your model. Profile and optimize operators in the graph. Optimize your model. Tweak the number of threads. Eliminate redundant copies.More items | |
C2720 | A cross-sectional study selects a single group, for whose members the presence or absence of the condition is initially unknown, and looks for correlations between current characteristics (and/or retrospectively recalled past characteristics) and presence/absence of the condition of interest. | |
C2721 | Correlation is a statistical method used to determine whether a relationship between variables exists. Regression is a statistical method used to describe the nature of the relationship between variables — i.e., a positive or negative, linear or nonlinear relationship. | |
C2722 | Multidimensional scaling (MDS) is a means of visualizing the level of similarity of individual cases of a dataset. More technically, MDS refers to a set of related ordination techniques used in information visualization, in particular to display the information contained in a distance matrix. | |
C2723 | deep learning - a name for an algorithm in machine learning (just like SVM, Regression etc.) transfer learning - as you may know, in order to train a Neural network it might take long time. So, we use a Neural Network that is already trained and in this way we can extract some features of new sample. | |
C2724 | Advantages of Neural Networks:Neural Networks have the ability to learn by themselves and produce the output that is not limited to the input provided to them.The input is stored in its own networks instead of a database, hence the loss of data does not affect its working.More items• | |
C2725 | Probability theory is the mathematical study of phenomena characterized by randomness or uncertainty. More precisely, probability is used for modelling situations when the result of an experiment, realized under the same circumstances, produces different results (typically throwing a dice or a coin). | |
C2726 | Random event/process/variable: an event/process that is not and cannot be made exact and, consequently, whose outcome cannot be predicted, e.g., the sum of the numbers on two rolled dice. | |
C2727 | This task of identifying the best subset of predictors to include in the model, among all possible subsets of predictors, is referred to as variable selection. | |
C2728 | Linear programming: The most widely used application of linear algebra is definitely optimization, and the most widely used kind of optimization is linear programming. You can optimize budgets, your diet, and your route to work using linear programming, and this only scratches the surface of the applications. | |
C2729 | Computer vision, however, is more than machine learning applied. It involves tasks as 3D scene modeling, multi-view camera geometry, structure-from-motion, stereo correspondence, point cloud processing, motion estimation and more, where machine learning is not a key element. | |
C2730 | For example, in regression analysis, many researchers say that there should be at least 10 observations per variable. If we are using three independent variables, then a clear rule would be to have a minimum sample size of 30. Some researchers follow a statistical formula to calculate the sample size. | |
C2731 | Information gain can also be used for feature selection, by evaluating the gain of each variable in the context of the target variable. In this slightly different usage, the calculation is referred to as mutual information between the two random variables. | |
C2732 | In simple words, stemming technique only looks at the form of the word whereas lemmatization technique looks at the meaning of the word. It means after applying lemmatization, we will always get a valid word. | |
C2733 | Assumptions for the Kruskal Wallis Test One independent variable with two or more levels (independent groups). The test is more commonly used when you have three or more levels. For two levels, consider using the Mann Whitney U Test instead. Ordinal scale, Ratio Scale or Interval scale dependent variables. | |
C2734 | The output from the logistic regression analysis gives a p-value of , which is based on the Wald z-score. Rather than the Wald method, the recommended method to calculate the p-value for logistic regression is the likelihood-ratio test (LRT), which for this data gives . | |
C2735 | Machine learning algorithms are the engines of machine learning, meaning it is the algorithms that turn a data set into a model. Which kind of algorithm works best (supervised, unsupervised, classification, regression, etc.) | |
C2736 | Cost function(J) of Linear Regression is the Root Mean Squared Error (RMSE) between predicted y value (pred) and true y value (y). Gradient Descent: To update θ1 and θ2 values in order to reduce Cost function (minimizing RMSE value) and achieving the best fit line the model uses Gradient Descent. | |
C2737 | Below are the steps to implement the handwritten digit recognition project:Import the libraries and load the dataset. First, we are going to import all the modules that we are going to need for training our model. Preprocess the data. Create the model. Train the model. Evaluate the model. Create GUI to predict digits. | |
C2738 | Some of the autonomous driving tasks where reinforcement learning could be applied include trajectory optimization, motion planning, dynamic pathing, controller optimization, and scenario-based learning policies for highways. For example, parking can be achieved by learning automatic parking policies. | |
C2739 | In machine learning, the hinge loss is a loss function used for training classifiers. The hinge loss is used for "maximum-margin" classification, most notably for support vector machines (SVMs). For an intended output t = ±1 and a classifier score y, the hinge loss of the prediction y is defined as. | |
C2740 | Stream processing is the processing of data in motion, or in other words, computing on data directly as it is produced or received. The majority of data are born as continuous streams: sensor events, user activity on a website, financial trades, and so on – all these data are created as a series of events over time. | |
C2741 | Bayesian networks are a type of Probabilistic Graphical Model that can be used to build models from data and/or expert opinion. They can be used for a wide range of tasks including prediction, anomaly detection, diagnostics, automated insight, reasoning, time series prediction and decision making under uncertainty. | |
C2742 | Humans are error-prone and biased, but that doesn't mean that algorithms are necessarily better. But these systems can be biased based on who builds them, how they're developed, and how they're ultimately used. This is commonly known as algorithmic bias. | |
C2743 | Dropout is a regularization method where input and recurrent connections to LSTM units are probabilistically excluded from activation and weight updates while training a network. This has the effect of reducing overfitting and improving model performance. | |
C2744 | in a test involving multiple comparisons, the probability of making at least one Type I error over an entire research study. The experiment-wise error rate differs from the testwise error rate, which is the probability of making a Type I error when performing a specific test or comparison. | |
C2745 | Gamma–Poisson mixture That is, we can view the negative binomial as a Poisson(λ) distribution, where λ is itself a random variable, distributed as a gamma distribution with shape = r and scale θ = p/(1 − p) or correspondingly rate β = (1 − p)/p. | |
C2746 | (Select all that apply.) Class boundaries are values halfway between the upper class limit of one class and the lower class limit of the next. Class limits specify the span of data values that fall within a class. | |
C2747 | Estimation theory is a branch of statistics that deals with estimating the values of parameters based on measured empirical data that has a random component. The parameters describe an underlying physical setting in such a way that their value affects the distribution of the measured data. | |
C2748 | Classification and regression trees are machine-learning methods for constructing. prediction models from data. The models are obtained by recursively partitioning. the data space and fitting a simple prediction model within each partition. | |
C2749 | In machine learning and natural language processing, a topic model is a type of statistical model for discovering the abstract "topics" that occur in a collection of documents. Topic modeling is a frequently used text-mining tool for discovery of hidden semantic structures in a text body. | |
C2750 | An SVM performs classification tasks by constructing hyperplanes in a multidimensional space that separates cases of different class labels. You can use an SVM when your data has exactly two classes, e.g. binary classification problems, but in this article we'll focus on a multi-class support vector machine in R. | |
C2751 | In these situations, the median is generally considered to be the best representative of the central location of the data. The more skewed the distribution, the greater the difference between the median and mean, and the greater emphasis should be placed on using the median as opposed to the mean. | |
C2752 | The F ratio is the ratio of two mean square values. If the null hypothesis is true, you expect F to have a value close to 1.0 most of the time. A large F ratio means that the variation among group means is more than you'd expect to see by chance. | |
C2753 | Keras is a neural network library while TensorFlow is the open-source library for a number of various tasks in machine learning. TensorFlow provides both high-level and low-level APIs while Keras provides only high-level APIs. Keras is built in Python which makes it way more user-friendly than TensorFlow. | |
C2754 | NHST is difficult to describe in one sentence, particularly here. | |
C2755 | ProcedureFrom the cluster management console, select Workload > Spark > Deep Learning.Select the Datasets tab.Click New.Create a dataset from Images for Object Detection.Provide a dataset name.Specify a Spark instance group.Provide a training folder. Provide the percentage of training images for validation.More items | |
C2756 | Based on Deep convolutional neural networks, DeepFace is a deep learning face recognition system. Created by Facebook, it detects and determines the identity of an individual's face through digital images, reportedly with an accuracy of 97.35%. | |
C2757 | 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. | |
C2758 | Chunking is used to add more structure to the sentence by following parts of speech (POS) tagging. It is also known as shallow parsing. Shallow Parsing is also called light parsing or chunking. The primary usage of chunking is to make a group of "noun phrases." The parts of speech are combined with regular expressions. | |
C2759 | c. Deep Learning lacks common sense. This makes the systems fragile and when errors are made, the errors can be very large. These are part of concerns and thus, there is a growing feeling in the field that deep learning's shortcomings require some fundamentally new ideas. | |
C2760 | Static final variables 2) The variable MY_VAR is public which means any class can use it. It is a static variable so you won't need any object of class in order to access it. It's final so the value of this variable can never be changed in the current or in any class. | |
C2761 | Random forest improves on bagging because it decorrelates the trees with the introduction of splitting on a random subset of features. This means that at each split of the tree, the model considers only a small subset of features rather than all of the features of the model. | |
C2762 | A two-sided hypothesis is an alternative hypothesis which is not bounded from above or from below, as opposed to a one-sided hypothesis which is always bounded from either above or below. In fact, a two-sided hypothesis is nothing more than the union of two one-sided hypotheses. | |
C2763 | Step 1: Find the mean of x, and the mean of y. Step 2: Subtract the mean of x from every x value (call them "a"), and subtract the mean of y from every y value (call them "b") Step 3: Calculate: ab, a2 and b2 for every value. Step 4: Sum up ab, sum up a2 and sum up b. | |
C2764 | Rejection region/Significance: P(x in rejection region|H0) = α. The p-value is a tool to check if the test statistic is in the rejection region. It is also a measure of the evidence for rejecting H0. “Data at least as extreme” is defined by the sidedness of the rejection region. | |
C2765 | Feature extraction identifies those product aspects which are being commented by customers, sentiment prediction identifies the text containing sentiment or opinion by deciding sentiment polarity as positive, negative or neutral and finally summarization module aggregates the results obtained from previous two steps. | |
C2766 | Support vectors are the elements of the training set that would change the position of the dividing hyperplane if removed. d+ = the shortest distance to the closest positive point d- = the shortest distance to the closest negative point The margin (gutter) of a separating hyperplane is d+ + d–. | |
C2767 | Divide the number of subjects by 2, and round down. In the example 5 ÷ 2 = 2.5 and rounding down gives 2. Find the first-ordered survival time that is greater than this number. This is the median survival time. | |
C2768 | Natural language refers to speech analysis in both audible speech, as well as text of a language. NLP systems capture meaning from an input of words (sentences, paragraphs, pages, etc.) in the form of a structured output (which varies greatly depending on the application). | |
C2769 | In a Data Mining sense, the similarity measure is a distance with dimensions describing object features. That means if the distance among two data points is small then there is a high degree of similarity among the objects and vice versa. The similarity is subjective and depends heavily on the context and application. | |
C2770 | The confidence of an association rule is a percentage value that shows how frequently the rule head occurs among all the groups containing the rule body. Thus, the confidence of a rule is the percentage equivalent of m/n, where the values are: m. The number of groups containing the joined rule head and rule body. | |
C2771 | Random errors in experimental measurements are caused by unknown and unpredictable changes in the experiment. Examples of causes of random errors are: electronic noise in the circuit of an electrical instrument, irregular changes in the heat loss rate from a solar collector due to changes in the wind. | |
C2772 | A bias vector is an additional set of weights in a neural network that require no input, and this it corresponds to the output of an artificial neural network when it has zero input. Bias represents an extra neuron included with each pre-output layer and stores the value of “1,” for each action. | |
C2773 | Below are the methods to convert a categorical (string) input to numerical nature:Label Encoder: It is used to transform non-numerical labels to numerical labels (or nominal categorical variables). Convert numeric bins to number: Let's say, bins of a continuous variable are available in the data set (shown below). | |
C2774 | The quantile function is the opposite of that. i.e. you give it a probability and it tells you the random variable value. So the median is the value of the quantile at the probability value of 0.5. A quartile is the value of the quantile at the probabilities 0.25, 0.5 and 0.75. | |
C2775 | In machine learning and statistics, the learning rate is a tuning parameter in an optimization algorithm that determines the step size at each iteration while moving toward a minimum of a loss function. In the adaptive control literature, the learning rate is commonly referred to as gain. | |
C2776 | Because our sample size is greater than 30, the Central Limit Theorem tells us that the sampling distribution will approximate a normal distribution. Because we know the population standard deviation and the sample size is large, we'll use the normal distribution to find probability. | |
C2777 | Cross Entropy is definitely a good loss function for Classification Problems, because it minimizes the distance between two probability distributions - predicted and actual. Consider a classifier which predicts whether the given animal is dog, cat or horse with a probability associated with each. | |
C2778 | Detection accuracy as discussed in this section refers to the agreement between the emotional states detected by different sets of emotion measurement equipment (e.g., multiple modalities), one of which is being used as the “grounded truth” (i.e., standard) for determining the correct emotion. | |
C2779 | In statistics, a positively skewed (or right-skewed) distribution is a type of distribution in which most values are clustered around the left tail of the distribution while the right tail of the distribution is longer. | |
C2780 | The normal distribution is the most important probability distribution in statistics because it fits many natural phenomena. For example, heights, blood pressure, measurement error, and IQ scores follow the normal distribution. | |
C2781 | To analyze this data follow these steps:Open the file KAPPA.SAV. Select Analyze/Descriptive Statistics/Crosstabs.Select Rater A as Row, Rater B as Col.Click on the Statistics button, select Kappa and Continue.Click OK to display the results for the Kappa test shown here: | |
C2782 | A feature vector is a vector containing multiple elements about an object. Putting feature vectors for objects together can make up a feature space. The features may represent, as a whole, one mere pixel or an entire image. The granularity depends on what someone is trying to learn or represent about the object. | |
C2783 | The variance is the average of the squared differences from the mean. Standard deviation is the square root of the variance so that the standard deviation would be about 3.03. Because of this squaring, the variance is no longer in the same unit of measurement as the original data. | |
C2784 | In behavioral finance, base rate fallacy is the tendency for people to erroneously judge the likelihood of a situation by not taking into account all relevant data. Instead, investors might focus more heavily on new information without acknowledging how this impacts original assumptions. | |
C2785 | Boosting refers to any Ensemble method that can combine several weak learners into a strong learner and is used to reduce bias and variance. Bagging otherwise known as bootstrap aggregating, is used to reduce variance which helps avoid overfitting. | |
C2786 | The problem is a paradox of the veridical type, because the correct choice (that one should switch doors) is so counterintuitive it can seem absurd, but is nevertheless demonstrably true. | |
C2787 | Meaning of Entropy At a conceptual level, Shannon's Entropy is simply the "amount of information" in a variable. More mundanely, that translates to the amount of storage (e.g. number of bits) required to store the variable, which can intuitively be understood to correspond to the amount of information in that variable. | |
C2788 | Generative Adversarial Networks, or GANs, are a deep-learning-based generative model. More generally, GANs are a model architecture for training a generative model, and it is most common to use deep learning models in this architecture. | |
C2789 | The term linear comes from algebra, because it's used to solve linear equation sets with multiple variables through operations such as subtracting one linear equation from another or multiplying it by a constant, results of which are linear equations. The left part of the equation is also known as a linear function. | |
C2790 | A training dataset is a dataset of examples used during the learning process and is used to fit the parameters (e.g., weights) of, for example, a classifier. | |
C2791 | Definition: Stratified sampling is a type of sampling method in which the total population is divided into smaller groups or strata to complete the sampling process. Stratified sampling is used when the researcher wants to understand the existing relationship between two groups. | |
C2792 | Random forests or random decision forests are an ensemble learning method for classification, regression and other tasks that operate by constructing a multitude of decision trees at training time and outputting the class that is the mode of the classes (classification) or mean/average prediction (regression) of the | |
C2793 | Bag of Words just creates a set of vectors containing the count of word occurrences in the document (reviews), while the TF-IDF model contains information on the more important words and the less important ones as well. Bag of Words vectors are easy to interpret. | |
C2794 | The monty hall problem has 3 doors instead of 100. It is still more likely that you pick a goat. If a person picks door 1 which is wrong the Monty Hall will close door 3 and give you chance to switch to the right answer, so it means they want always people win the prize. | |
C2795 | In order to assess practical significance, you would also want to know the effect size, strength of any relationship (through a correlation coefficient), and confidence intervals. That said, you would want to be careful not to “sanctify” any results (e.g. an effect size of . | |
C2796 | For an idea we are all familiar with, randomness is surprisingly hard to formally define. We think of a random process as something that evolves over time but in a way we can't predict. | |
C2797 | Latent Semantic Analysis is an efficient way of analysing the text and finding the hidden topics by understanding the context of the text. Latent Semantic Analysis(LSA) is used to find the hidden topics represented by the document or text. This hidden topics then are used for clustering the similar documents together. | |
C2798 | Examples of sampling bias include self-selection, pre-screening of trial participants, discounting trial subjects/tests that did not run to completion and migration bias by excluding subjects who have recently moved into or out of the study area. | |
C2799 | Statistical inference can be divided into two areas: estimation and hypothesis testing. In estimation, the goal is to describe an unknown aspect of a population, for example, the average scholastic aptitude test (SAT) writing score of all examinees in the State of California in the USA. |
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