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C4300 | A confusion matrix is a table that is often used to describe the performance of a classification model (or "classifier") on a set of test data for which the true values are known. The confusion matrix itself is relatively simple to understand, but the related terminology can be confusing. | |
C4301 | 2.4. 7 Cosine Similarity Cosine similarity measures the similarity between two vectors of an inner product space. It is measured by the cosine of the angle between two vectors and determines whether two vectors are pointing in roughly the same direction. It is often used to measure document similarity in text analysis. | |
C4302 | 1:0827:37Suggested clip · 115 secondsCreating a Dataset and training an Artificial Neural Network with KerasYouTubeStart of suggested clipEnd of suggested clip | |
C4303 | Data smoothing uses an algorithm to remove noise from a data set, allowing important patterns to stand out. It can be used to predict trends, such as those found in securities prices. Different data smoothing models include the random method, random walk, and the moving average. | |
C4304 | Both indices take values from zero to one. In a similarity index, a value of 1 means that the two communities you are comparing share all their species, while a value of 0 means they share none. In a dissimilarity index the interpretation is the opposite: 1 means that the communities are totally different. | |
C4305 | Max pooling is a sample-based discretization process. The objective is to down-sample an input representation (image, hidden-layer output matrix, etc.), reducing its dimensionality and allowing for assumptions to be made about features contained in the sub-regions binned. | |
C4306 | Covariance is when two variables vary with each other, whereas Correlation is when the change in one variable results in the change in another variable.Differences between Covariance and Correlation.CovarianceCorrelationCovariance can vary between -∞ and +∞Correlation ranges between -1 and +17 more rows• | |
C4307 | One reason you should consider when using ReLUs is, that they can produce dead neurons. That means that under certain circumstances your network can produce regions in which the network won't update, and the output is always 0. | |
C4308 | A continuous sample space is based on the same principles, but it has an infinite number of items in the space. In other words, you can't write out the space in the same way that you would write out the sample space for a die roll. | |
C4309 | Z score is used when: the data follows a normal distribution, when you know the standard deviation of the population and your sample size is above 30. T-Score - is used when you have a smaller sample <30 and you have an unknown population standard deviation. | |
C4310 | The AUC value lies between 0.5 to 1 where 0.5 denotes a bad classifer and 1 denotes an excellent classifier. | |
C4311 | A core characteristic of non-probability sampling techniques is that samples are selected based on the subjective judgement of the researcher, rather than random selection (i.e., probabilistic methods), which is the cornerstone of probability sampling techniques. | |
C4312 | The Two-Sample assuming Equal Variances test is used when you know (either through the question or you have analyzed the variance in the data) that the variances are the same. The Two-Sample assuming UNequal Variances test is used when either: You know the variances are not the same. | |
C4313 | A common application is to take the standard deviation of the last 20 periods, multiply it by 1.5 and add that amount to the average value. Whenever the value of your time series data crosses above that value then that would indicate an upward trend. Likewise a lower Bollinger band can used to identify a down trend. | |
C4314 | Linear time invariant (LTI) filters are linear applications that transform a signal into another signal, as such that the application commutes with time shifts. | |
C4315 | Two types of cross-validation can be distinguished: exhaustive and non-exhaustive cross-validation.Exhaustive cross-validation.Non-exhaustive cross-validation.k*l-fold cross-validation.k-fold cross-validation with validation and test set. | |
C4316 | Time series analysis can be useful to see how a given asset, security, or economic variable changes over time. It can also be used to examine how the changes associated with the chosen data point compare to shifts in other variables over the same time period. | |
C4317 | In machine learning, the perceptron is an algorithm for supervised learning of binary classifiers. It is a type of linear classifier, i.e. a classification algorithm that makes its predictions based on a linear predictor function combining a set of weights with the feature vector. | |
C4318 | If outcomes are equally likely, then the probability of an event occurring is the number in the event divided by the number in the sample space. The probability of rolling a six on a single roll of a die is 1/6 because there is only 1 way to roll a six out of 6 ways it could be rolled. | |
C4319 | Whole Numbers {0, 1, 2, 3, 4…..} These include the natural (counting) numbers, but they also include zero. | |
C4320 | 1. The mean of the distribution of sample means is called the Expected Value of M and is always equal to the population mean μ. 3. | |
C4321 | Selectors are the names given to styles in internal and external style sheets. In this CSS Beginner Tutorial we will be concentrating on HTML selectors, which are simply the names of HTML tags and are used to change the style of a specific type of element. | |
C4322 | Examples of Predictive AnalyticsRetail. Probably the largest sector to use predictive analytics, retail is always looking to improve its sales position and forge better relations with customers. Health. Sports. Weather. Insurance/Risk Assessment. Financial modeling. Energy. Social Media Analysis.More items• | |
C4323 | There are multiple ways to select a good starting point for the learning rate. A naive approach is to try a few different values and see which one gives you the best loss without sacrificing speed of training. We might start with a large value like 0.1, then try exponentially lower values: 0.01, 0.001, etc. | |
C4324 | PGMs with undirected edges are known as Markov networks (MNs) or Markov random fields (MRFs). | |
C4325 | In case of mean and median, it is not necessary. However, the accuracy of the mean would be higher if the class intervals are short. Similarly the median would be more accurate if the 'median class', class interval in which median falls, is of short length. | |
C4326 | Spearman Rank Correlation: Worked Example (No Tied Ranks)The formula for the Spearman rank correlation coefficient when there are no tied ranks is: Step 1: Find the ranks for each individual subject. Step 2: Add a third column, d, to your data. Step 5: Insert the values into the formula.More items• | |
C4327 | So, if your significance level is 0.05, the corresponding confidence level is 95%. If the P value is less than your significance (alpha) level, the hypothesis test is statistically significant. If the confidence interval does not contain the null hypothesis value, the results are statistically significant. | |
C4328 | Generative adversarial nets can be applied in many fields from generating images to predicting drugs, so don't be afraid of experimenting with them. We believe they help in building a better future for machine learning. | |
C4329 | The V-model is an SDLC model where execution of processes happens in a sequential manner in a V-shape. It is also known as Verification and Validation model. The V-Model is an extension of the waterfall model and is based on the association of a testing phase for each corresponding development stage. | |
C4330 | The pdf and cdf give a complete description of the probability distribution of a random variable. The pdf represents the relative frequency of failure times as a function of time. The cdf is a function, F(x)\,\!, of a random variable X\,\!, and is defined for a number x\,\! by: F(x)=P(X\le x)=\int_{0}^{x}f(s)ds\ \,\! | |
C4331 | Feature Extraction using Convolution Neural Networks (CNN) and Deep Learning. It is a process which involves the following tasks of pre-processing the image (normalization), image segmentation, extraction of key features and identification of the class. | |
C4332 | The null hypothesis (H0) for a one tailed test is that the mean is greater (or less) than or equal to µ, and the alternative hypothesis is that the mean is < (or >, respectively) µ. | |
C4333 | Mean Square Error (MSE) is the most commonly used regression loss function. MSE is the sum of squared distances between our target variable and predicted values. The MSE loss (Y-axis) reaches its minimum value at prediction (X-axis) = 100. The range is 0 to ∞. | |
C4334 | In research, an experimenter bias, also known as research bias, occurs when a researcher unconsciously affects results, data, or a participant in an experiment due to subjective influence. It is very important to consider experimenter bias as a possible issue in any research setting. | |
C4335 | Parametric tests assume underlying statistical distributions in the data. Nonparametric tests do not rely on any distribution. They can thus be applied even if parametric conditions of validity are not met. | |
C4336 | Use systematic sampling when there's low risk of data manipulation. Systematic sampling is the preferred method over simple random sampling when a study maintains a low risk of data manipulation. | |
C4337 | According to Accenture's Technology Vision 2017, AI has the potential to double annual economic growth rates by 2035. To avoid missing out on this opportunity, policy makers and business leaders must prepare for, and work toward, a future with artificial intelligence. | |
C4338 | Hold-out is when you split up your dataset into a 'train' and 'test' set. The training set is what the model is trained on, and the test set is used to see how well that model performs on unseen data. | |
C4339 | The general algorithm is The Backpropagation algorithm is suitable for the feed forward neural network on fixed sized input-output pairs. The Backpropagation Through Time is the application of Backpropagation training algorithm which is applied to the sequence data like the time series. | |
C4340 | 1 Answer. The difference between a classification and regression is that a classification outputs a prediction probability for class/classes and regression provides a value. We can make a neural network to output a value by simply changing the activation function in the final layer to output the values. | |
C4341 | AUC (Area under the ROC Curve). AUC provides an aggregate measure of performance across all possible classification thresholds. One way of interpreting AUC is as the probability that the model ranks a random positive example more highly than a random negative example. | |
C4342 | The short answer is: Logistic regression is considered a generalized linear model because the outcome always depends on the sum of the inputs and parameters. Or in other words, the output cannot depend on the product (or quotient, etc.) | |
C4343 | The kurtosis of any univariate normal distribution is 3. It is common to compare the kurtosis of a distribution to this value. Distributions with kurtosis less than 3 are said to be platykurtic, although this does not imply the distribution is "flat-topped" as is sometimes stated. | |
C4344 | “Statistical significance helps quantify whether a result is likely due to chance or to some factor of interest,” says Redman. | |
C4345 | Why is an alpha level of . 05 commonly used? Seeing as the alpha level is the probability of making a Type I error, it seems to make sense that we make this area as tiny as possible. The smaller the alpha level, the smaller the area where you would reject the null hypothesis. | |
C4346 | The mean is more commonly known as the average. The median is the mid-point in a distribution of values among cases, with an equal number of cases above and below the median. The mode is the value that occurs most often in the distribution. | |
C4347 | The Distributional Hypothesis is that words that occur in the same contexts tend to have similar meanings (Harris, 1954). Although the Distributional Hypothesis originated in Linguistics, it is now receiving attention in Cognitive Science (McDonald and Ramscar, 2001). | |
C4348 | March 2016 | |
C4349 | In machine learning, the polynomial kernel is a kernel function commonly used with support vector machines (SVMs) and other kernelized models, that represents the similarity of vectors (training samples) in a feature space over polynomials of the original variables, allowing learning of non-linear models. | |
C4350 | Try to understand the basic of the data structure first like the basic topics like the Stack, queue, list, tree, graph, etc. Start practicing the algorithm and just try to solve the basic algorithm problems. Google the topics that you are learning and just watch the you tube videos. | |
C4351 | Having an antibody test too early can lead to false negative results. That's because it takes a week or two after infection for your immune system to produce antibodies. The reported rate of false negatives is 20%. | |
C4352 | The coefficient of a continuous predictor is the estimated change in the natural log of the odds for the reference event for each unit increase in the predictor. | |
C4353 | Analysis of Variance (ANOVA) is a statistical method used to test differences between two or more means. It may seem odd that the technique is called "Analysis of Variance" rather than "Analysis of Means." As you will see, the name is appropriate because inferences about means are made by analyzing variance. | |
C4354 | It is the limit of the probability of the interval (x,x+Δ] divided by the length of the interval as the length of the interval goes to 0. Remember that P(x<X≤x+Δ)=FX(x+Δ)−FX(x). =dFX(x)dx=F′X(x),if FX(x) is differentiable at x. is called the probability density function (PDF) of X. | |
C4355 | The short answer is: Logistic regression is considered a generalized linear model because the outcome always depends on the sum of the inputs and parameters. Or in other words, the output cannot depend on the product (or quotient, etc.) | |
C4356 | In order to choose the support vectors, we want to maximize the margin m and that implies we reduce the magnitude or norm of the vector that's perpendicular to the hyperplanes(s) and closest to a datapoint. which implies that the lower the norm of vector w, then greater is the margin. | |
C4357 | Best Practices of Data CleaningSetting up a Quality Plan. RELATED BLOG. Fill-out missing values. One of the first steps of fixing errors in your dataset is to find incomplete values and fill them out. Removing rows with missing values. Fixing errors in the structure. Reducing data for proper data handling. | |
C4358 | "A Bayesian network is a probabilistic graphical model which represents a set of variables and their conditional dependencies using a directed acyclic graph." It is also called a Bayes network, belief network, decision network, or Bayesian model. | |
C4359 | To find the relative frequency, divide the frequency by the total number of data values. To find the cumulative relative frequency, add all of the previous relative frequencies to the relative frequency for the current row. | |
C4360 | AI would have a low error rate compared to humans, if coded properly. They would have incredible precision, accuracy, and speed. They won't be affected by hostile environments, thus able to complete dangerous tasks, explore in space, and endure problems that would injure or kill us. | |
C4361 | If a confusion matrix threshold is at disposal, instead, we recommend the usage of the Matthews correlation coefficient over F1 score, and accuracy. We decided to focus on accuracy and F1 score because they are the most common metrics used for binary classification in machine learning. | |
C4362 | A chi-square (χ2) statistic is a test that measures how a model compares to actual observed data. The chi-square statistic compares the size any discrepancies between the expected results and the actual results, given the size of the sample and the number of variables in the relationship. | |
C4363 | The idea of mean filtering is simply to replace each pixel value in an image with the mean (`average') value of its neighbors, including itself. This has the effect of eliminating pixel values which are unrepresentative of their surroundings. Mean filtering is usually thought of as a convolution filter. | |
C4364 | Multiclass classification with logistic regression can be done either through the one-vs-rest scheme in which for each class a binary classification problem of data belonging or not to that class is done, or changing the loss function to cross- entropy loss. By default, multi_class is set to 'ovr'. | |
C4365 | In computer vision, the bag-of-words model (BoW model) sometimes called bag-of-visual-words model can be applied to image classification, by treating image features as words. In document classification, a bag of words is a sparse vector of occurrence counts of words; that is, a sparse histogram over the vocabulary. | |
C4366 | A hierarchical model is a model in which lower levels are sorted under a hierarchy of successively higher-level units. Data is grouped into clusters at one or more levels, and the influence of the clusters on the data points contained in them is taken account in any statistical analysis. | |
C4367 | Any good analysis of survey data from a stratified sample includes the same seven steps:Estimate a population parameter.Compute sample variance within each stratum.Compute standard error.Specify a confidence level.Find the critical value (often a z-score or a t-score).Compute margin of error.More items | |
C4368 | A Markov chain in which every state can be reached from every other state is called an irreducible Markov chain. If a Markov chain is not irreducible, but absorbable, the sequences of microscopic states may be trapped into some independent closed states and never escape from such undesirable states. | |
C4369 | Unsupervised learning is a type of machine learning algorithm used to draw inferences from datasets consisting of input data without labeled responses. The most common unsupervised learning method is cluster analysis, which is used for exploratory data analysis to find hidden patterns or grouping in data. | |
C4370 | Elman neural network (ENN) is one of recurrent neural networks (RNNs). Comparing to traditional neural networks, ENN has additional inputs from the hidden layer, which forms a new layer–the context layer. So the standard back-propagation (BP) algorithm used in ENN is called Elman back-propagation algorithm (EBP). | |
C4371 | In the context of CNN, a filter is a set of learnable weights which are learned using the backpropagation algorithm. You can think of each filter as storing a single template/pattern. Filter is referred to as a set of shared weights on the input. | |
C4372 | Parametric tests assume underlying statistical distributions in the data. Nonparametric tests do not rely on any distribution. They can thus be applied even if parametric conditions of validity are not met. | |
C4373 | Two random variables X and Y are said to be bivariate normal, or jointly normal, if aX+bY has a normal distribution for all a,b∈R. In the above definition, if we let a=b=0, then aX+bY=0. We agree that the constant zero is a normal random variable with mean and variance 0. | |
C4374 | 11 websites to find free, interesting datasetsFiveThirtyEight. BuzzFeed News. Kaggle. Socrata. Awesome-Public-Datasets on Github. Google Public Datasets. UCI Machine Learning Repository. Data.gov.More items | |
C4375 | A method of computing a kind of arithmetic mean of a set of numbers in which some elements of the set carry more importance (weight) than others. Example: Grades are often computed using a weighted average. Suppose that homework counts 10%, quizzes 20%, and tests 70%. | |
C4376 | Feature Scaling is a technique to standardize the independent features present in the data in a fixed range. It is performed during the data pre-processing to handle highly varying magnitudes or values or units. So, we use Feature Scaling to bring all values to same magnitudes and thus, tackle this issue. | |
C4377 | In the case of multiclass classification, a typically used loss function is the Hard Loss Function [29, 36, 61], which counts the number of misclassifications: ℓ(f, z) = ℓH(f, z) = [f(x)≠y]. | |
C4378 | In many applications including econometrics and biostatistics a fixed effects model refers to a regression model in which the group means are fixed (non-random) as opposed to a random effects model in which the group means are a random sample from a population. | |
C4379 | Prior: Probability distribution representing knowledge or uncertainty of a data object prior or before observing it. Posterior: Conditional probability distribution representing what parameters are likely after observing the data object. Likelihood: The probability of falling under a specific category or class. | |
C4380 | LSI Graph is a free LSI keyword tool designed to help you identify dozens of related terms to use in your copy. Visit the website and enter your target keyword to generate a long list of potential LSI keywords. When you have a long list of LSI keywords, it may be tempting to use as many as possible in your content. | |
C4381 | Statistical inference involves hypothesis testing (evaluating some idea about a population using a sample) and estimation (estimating the value or potential range of values of some characteristic of the population based on that of a sample). | |
C4382 | Estimating the disparity field between two stereo images is a common task in computer vision, e.g., to determine a dense depth map. Variational methods currently are among the most accurate techniques for dense disparity map reconstruction. | |
C4383 | Convolutional layers are different in that they have a fixed number of weights governed by the choice of filter size and number of filters, but independent of the input size. The filter weights absolutely must be updated in backpropagation, since this is how they learn to recognize features of the input. | |
C4384 | The difference between MLE/MAP and Bayesian inference MLE gives you the value which maximises the Likelihood P(D|θ). And MAP gives you the value which maximises the posterior probability P(θ|D). As both methods give you a single fixed value, they're considered as point estimators. | |
C4385 | Because our sample size is large. It is called the standard error because it refers to how much the sample mean fluctuates or is in error around the actual population mean. | |
C4386 | In practical terms, deep learning is just a subset of machine learning. In fact, deep learning technically is machine learning and functions in a similar way (hence why the terms are sometimes loosely interchanged). | |
C4387 | In the context of market research, a sampling unit is an individual person. The term sampling unit refers to a singular value within a sample database. For example, if you were conducting research using a sample of university students, a single university student would be a sampling unit. | |
C4388 | The infinite impulse response (IIR) filter is a recursive filter in that the output from the filter is computed by using the current and previous inputs and previous outputs. Because the filter uses previous values of the output, there is feedback of the output in the filter structure. | |
C4389 | The Loss Function is one of the important components of Neural Networks. Loss is nothing but a prediction error of Neural Net. And the method to calculate the loss is called Loss Function. In simple words, the Loss is used to calculate the gradients. And gradients are used to update the weights of the Neural Net. | |
C4390 | A nocebo effect is said to occur when negative expectations of the patient regarding a treatment cause the treatment to have a more negative effect than it otherwise would have. | |
C4391 | Definition. A convenience sample is a type of non-probability sampling method where the sample is taken from a group of people easy to contact or to reach. For example, standing at a mall or a grocery store and asking people to answer questions would be an example of a convenience sample. | |
C4392 | Definition: Quota sampling is a sampling methodology wherein data is collected from a homogeneous group. It involves a two-step process where two variables can be used to filter information from the population. It can easily be administered and helps in quick comparison. | |
C4393 | A random variable is a numerical description of the outcome of a statistical experiment. A random variable that may assume only a finite number or an infinite sequence of values is said to be discrete; one that may assume any value in some interval on the real number line is said to be continuous. | |
C4394 | Alpha sets the standard for how extreme the data must be before we can reject the null hypothesis. The p-value indicates how extreme the data are. If the p-value is greater than alpha (p > . 05), then we fail to reject the null hypothesis, and we say that the result is statistically nonsignificant (n.s.). | |
C4395 | Data mining relies heavily on programming, and yet there's no conclusion which is the best language for data mining. It all depends on the dataset you deal with. Most languages can fall somewhere on the map. R and Python are the most popular programming languages for data science, according to research from KD Nuggets. | |
C4396 | Bootstrapping is any test or metric that uses random sampling with replacement, and falls under the broader class of resampling methods. Bootstrapping assigns measures of accuracy (bias, variance, confidence intervals, prediction error, etc.) to sample estimates. | |
C4397 | An internal covariate shift occurs when there is a change in the input distribution to our network. When the input distribution changes, hidden layers try to learn to adapt to the new distribution. This slows down the training process. | |
C4398 | The z-score is positive if the value lies above the mean, and negative if it lies below the mean. It is also known as a standard score, because it allows comparison of scores on different kinds of variables by standardizing the distribution. | |
C4399 | In a positively skewed distribution the outliers will be pulling the mean down the scale a great deal. The median might be slightly lower due to the outlier, but the mode will be unaffected. Thus, with a negatively skewed distribution the mean is numerically lower than the median or mode. |
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