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C8500 | Standard deviation is sensitive to outliers. A single outlier can raise the standard deviation and in turn, distort the picture of spread. For data with approximately the same mean, the greater the spread, the greater the standard deviation. | |
C8501 | Padding is a term relevant to convolutional neural networks as it refers to the amount of pixels added to an image when it is being processed by the kernel of a CNN. For example, if the padding in a CNN is set to zero, then every pixel value that is added will be of value zero. | |
C8502 | A probability distribution is a statistical function that describes all the possible values and likelihoods that a random variable can take within a given range. These factors include the distribution's mean (average), standard deviation, skewness, and kurtosis. | |
C8503 | Linear least squares regression is by far the most widely used modeling method. It is what most people mean when they say they have used "regression", "linear regression" or "least squares" to fit a model to their data. | |
C8504 | In an economic model, an exogenous variable is one whose value is determined outside the model and is imposed on the model, and an exogenous change is a change in an exogenous variable. In contrast, an endogenous variable is a variable whose value is determined by the model. | |
C8505 | Chisquare Test, Different Types and its Application using RChi-Square Test.Chi-square test of independence.2 x 2 Contingency Table.Chi-square test of significance.Chi-square Test in R.Chi Square Goodness of Fit (One Sample Test)Chi-square Goodness of Test in R.Fisher's exact test.More items• | |
C8506 | Divide the number of events by the number of possible outcomes.Determine a single event with a single outcome. Identify the total number of outcomes that can occur. Divide the number of events by the number of possible outcomes. Determine each event you will calculate. Calculate the probability of each event.More items•6 days ago | |
C8507 | Bag-of-words refers to what kind of information you can extract from a document (namely, unigram words). Vector space model refers to the data structure for each document (namely, a feature vector of term & term weight pairs). Only the unigram words themselves, making for a bunch of words to represent the document. | |
C8508 | Examples. According to the law of large numbers, if a large number of six-sided dice are rolled, the average of their values (sometimes called the sample mean) is likely to be close to 3.5, with the precision increasing as more dice are rolled. | |
C8509 | The additive effect of allele M2 is the average change in genotypic values seen by substituting an M2 allele for an M1 allele. To find this effect, simply construct a new variable, called X1 here, that equals the number of M2 alleles for the individual's genotype. | |
C8510 | Steps for Making decision treeGet list of rows (dataset) which are taken into consideration for making decision tree (recursively at each nodes).Calculate uncertanity of our dataset or Gini impurity or how much our data is mixed up etc.Generate list of all question which needs to be asked at that node.More items• | |
C8511 | Summary. The k-nearest neighbors (KNN) algorithm is a simple, supervised machine learning algorithm that can be used to solve both classification and regression problems. It's easy to implement and understand, but has a major drawback of becoming significantly slows as the size of that data in use grows. | |
C8512 | Residual = Observed – Predicted positive values for the residual (on the y-axis) mean the prediction was too low, and negative values mean the prediction was too high; 0 means the guess was exactly correct. | |
C8513 | Convolutional Neural Networks | |
C8514 | Multicollinearity happens when one predictor variable in a multiple regression model can be linearly predicted from the others with a high degree of accuracy. This can lead to skewed or misleading results. Luckily, decision trees and boosted trees algorithms are immune to multicollinearity by nature . | |
C8515 | In statistics, a population is the entire pool from which a statistical sample is drawn. A population may refer to an entire group of people, objects, events, hospital visits, or measurements. A population can thus be said to be an aggregate observation of subjects grouped together by a common feature. | |
C8516 | In Vapnik–Chervonenkis theory, the Vapnik–Chervonenkis (VC) dimension is a measure of the capacity (complexity, expressive power, richness, or flexibility) of a set of functions that can be learned by a statistical binary classification algorithm. It was originally defined by Vladimir Vapnik and Alexey Chervonenkis. | |
C8517 | What is a term document matrix?Clean your text responses using Insert > More > Text Analysis > Setup Text Analysis. Add your term-document matrix using Insert > More > Text Analysis > Techniques > Create Term Document Matrix. | |
C8518 | The classification report visualizer displays the precision, recall, F1, and support scores for the model. There are four ways to check if the predictions are right or wrong: TN / True Negative: the case was negative and predicted negative. TP / True Positive: the case was positive and predicted positive. | |
C8519 | A non-stationary process with a deterministic trend has a mean that grows around a fixed trend, which is constant and independent of time. It specifies the value at time "t" by the last period's value, a drift, a trend, and a stochastic component. | |
C8520 | Answered March 1, 2016. Differentiability is the only condition of an activation function. | |
C8521 | Poisson regression is used to predict a dependent variable that consists of "count data" given one or more independent variables. The variable we want to predict is called the dependent variable (or sometimes the response, outcome, target or criterion variable). | |
C8522 | Train and serve a TensorFlow model with TensorFlow ServingTable of contents.Create your model. Import the Fashion MNIST dataset. Train and evaluate your model.Save your model.Examine your saved model.Serve your model with TensorFlow Serving. Add TensorFlow Serving distribution URI as a package source: Make a request to your model in TensorFlow Serving. Make REST requests. | |
C8523 | The Fourier Series is a specialized tool that allows for any periodic signal (subject to certain conditions) to be decomposed into an infinite sum of everlasting sinusoids. Practically, this allows the user of the Fourier Series to understand a periodic signal as the sum of various frequency components. | |
C8524 | The notion of the distance matrix between individual points is not particularly useful in k-means clustering. The matrix of distances between data points and the centroids is, however, quite central. | |
C8525 | Q17. Which of the following is true about “Ridge” or “Lasso” regression methods in case of feature selection? “Ridge regression” will use all predictors in final model whereas “Lasso regression” can be used for feature selection because coefficient values can be zero. | |
C8526 | Bootstrap aggregating, also called bagging (from bootstrap aggregating), is a machine learning ensemble meta-algorithm designed to improve the stability and accuracy of machine learning algorithms used in statistical classification and regression. It also reduces variance and helps to avoid overfitting. | |
C8527 | Y-hat = b0 + b1(x) - This is the sample regression line. You must calculate b0 & b1 to create this line. Y-hat stands for the predicted value of Y, and it can be obtained by plugging an individual value of x into the equation and calculating y-hat. | |
C8528 | TensorFlow is more of a low-level library; basically, we can think of TensorFlow as the Lego bricks (similar to NumPy and SciPy) that we can use to implement machine learning algorithms whereas scikit-learn comes with off-the-shelf algorithms, e.g., algorithms for classification such as SVMs, Random Forests, Logistic | |
C8529 | Inferential statistics helps to suggest explanations for a situation or phenomenon. It allows you to draw conclusions based on extrapolations, and is in that way fundamentally different from descriptive statistics that merely summarize the data that has actually been measured. | |
C8530 | Sentiment analysis – otherwise known as opinion mining – is a much bandied about but often misunderstood term. In essence, it is the process of determining the emotional tone behind a series of words, used to gain an understanding of the the attitudes, opinions and emotions expressed within an online mention. | |
C8531 | Probability distributions are a fundamental concept in statistics. They are used both on a theoretical level and a practical level. Some practical uses of probability distributions are: To calculate confidence intervals for parameters and to calculate critical regions for hypothesis tests. | |
C8532 | A fast Fourier transform (FFT) is an algorithm that computes the discrete Fourier transform (DFT) of a sequence, or its inverse (IDFT). Fourier analysis converts a signal from its original domain (often time or space) to a representation in the frequency domain and vice versa. | |
C8533 | If the limit of |a[n+1]/a[n]| is less than 1, then the series (absolutely) converges. If the limit is larger than one, or infinite, then the series diverges. | |
C8534 | Five Common Types of Sampling ErrorsPopulation Specification Error—This error occurs when the researcher does not understand who they should survey. Sample Frame Error—A frame error occurs when the wrong sub-population is used to select a sample.More items | |
C8535 | Yes, it can be used for both continuous and categorical target (dependent) variable. In random forest/decision tree, classification model refers to factor/categorical dependent variable and regression model refers to numeric or continuous dependent variable. | |
C8536 | The term ''mixed model'' refers to the inclusion of both fixed effects, which are model components used to define systematic relationships such as overall changes over time and/ or experimentally induced group differences; and random effects, which account for variability among subjects around the systematic | |
C8537 | Outliers may be plotted as individual points. Box plots are non-parametric: they display variation in samples of a statistical population without making any assumptions of the underlying statistical distribution (though Tukey's boxplot assumes symmetry for the whiskers and normality for their length). | |
C8538 | The expected value is simply a way to describe the average of a discrete set of variables based on their associated probabilities. This is also known as a probability-weighted average. | |
C8539 | A Correlation of 0 means that there is no linear relationship between the two variables. We already know that if two random variables are independent, the Covariance is 0. We can see that if we plug in 0 for the Covariance to the equation for Correlation, we will get a 0 for the Correlation. | |
C8540 | Definition: Given data the maximum likelihood estimate (MLE) for the parameter p is the value of p that maximizes the likelihood P(data |p). That is, the MLE is the value of p for which the data is most likely. 100 P(55 heads|p) = ( 55 ) p55(1 − p)45. | |
C8541 | A false positive is an outcome where the model incorrectly predicts the positive class. And a false negative is an outcome where the model incorrectly predicts the negative class. In the following sections, we'll look at how to evaluate classification models using metrics derived from these four outcomes. | |
C8542 | 2:1422:33Suggested clip · 114 secondsRegression Trees, Clearly Explained!!! - YouTubeYouTubeStart of suggested clipEnd of suggested clip | |
C8543 | Convergence in probability implies convergence in distribution. In the opposite direction, convergence in distribution implies convergence in probability when the limiting random variable X is a constant. Convergence in probability does not imply almost sure convergence. | |
C8544 | AS a general thumb rule if adjusted R 2 increases when a new variables is added to the model, the variable should remain in the model. If the adjusted R2 decreases when the new variable is added then the variable should not remain in the model. | |
C8545 | 4 Answers. Linear SVMs and logistic regression generally perform comparably in practice. Use SVM with a nonlinear kernel if you have reason to believe your data won't be linearly separable (or you need to be more robust to outliers than LR will normally tolerate). | |
C8546 | Signal detection assumes that there is “noise” in any system. In this example, if we have an old car, we may hear clunks even when the car is operating effectively, or even tinnitus in our ear, or something rustling in the trunk. The signal is what you are trying to detect. | |
C8547 | In probability theory and statistics, the exponential distribution is the probability distribution of the time between events in a Poisson point process, i.e., a process in which events occur continuously and independently at a constant average rate. | |
C8548 | In a supervised learning model, the algorithm learns on a labeled dataset, providing an answer key that the algorithm can use to evaluate its accuracy on training data. An unsupervised model, in contrast, provides unlabeled data that the algorithm tries to make sense of by extracting features and patterns on its own. | |
C8549 | Test-retest reliability example You administer the test two months apart to the same group of people, but the results are significantly different, so the test-retest reliability of the IQ questionnaire is low. | |
C8550 | Reduce Variance of an Estimate If we want to reduce the amount of variance in a prediction, we must add bias. Consider the case of a simple statistical estimate of a population parameter, such as estimating the mean from a small random sample of data. A single estimate of the mean will have high variance and low bias. | |
C8551 | A null hypothesis is a type of hypothesis used in statistics that proposes that there is no difference between certain characteristics of a population (or data-generating process). | |
C8552 | Data Parallelism means concurrent execution of the same task on each multiple computing core. Let's take an example, summing the contents of an array of size N. For a single-core system, one thread would simply sum the elements [0] . . . So the Two threads would be running in parallel on separate computing cores. | |
C8553 | Overview of stacking. Stacking mainly differ from bagging and boosting on two points. Second, stacking learns to combine the base models using a meta-model whereas bagging and boosting combine weak learners following deterministic algorithms. | |
C8554 | Scatter plots' primary uses are to observe and show relationships between two numeric variables. The dots in a scatter plot not only report the values of individual data points, but also patterns when the data are taken as a whole. A scatter plot can also be useful for identifying other patterns in data. | |
C8555 | 1 Answer. 0 is a rational, whole, integer and real number. Some definitions include it as a natural number and some don't (starting at 1 instead). | |
C8556 | In mathematics, a Fourier transform (FT) is a mathematical transform that decomposes a function (often a function of time, or a signal) into its constituent frequencies, such as the expression of a musical chord in terms of the volumes and frequencies of its constituent notes. | |
C8557 | Deep reinforcement learning is a category of machine learning and artificial intelligence where intelligent machines can learn from their actions similar to the way humans learn from experience. Actions that get them to the target outcome are rewarded (reinforced). | |
C8558 | FDR is a very simple concept. It is the number of false discoveries in an experiment divided by total number of discoveries in that experiment. (You calculate one P-value for each sample or test in your experiment.) | |
C8559 | Bagging is a way to decrease the variance in the prediction by generating additional data for training from dataset using combinations with repetitions to produce multi-sets of the original data. Boosting is an iterative technique which adjusts the weight of an observation based on the last classification. | |
C8560 | Cluster sampling is typically used in market research. It's used when a researcher can't get information about the population as a whole, but they can get information about the clusters. For example, a researcher may be interested in data about city taxes in Florida. | |
C8561 | Covariance is the measure of how much two sets of data vary. The Covariance determines the degree to which the two variables are related or how they vary together. The Covariance is the average of the product of deviations of data points from their respective means, based on the following formula. | |
C8562 | The availability heuristic is a mental shortcut that helps us make a decision based on how easy it is to bring something to mind. The representativeness heuristic is a mental shortcut that helps us make a decision by comparing information to our mental prototypes. | |
C8563 | There is a clear difference between variables and parameters. A variable represents a model state, and may change during simulation. A parameter is commonly used to describe objects statically. A parameter is normally a constant in a single simulation, and is changed only when you need to adjust your model behavior. | |
C8564 | There is no direct evidence that the brain uses a backprop-like algorithm for learning. Past work has shown, however, that backprop-trained models can account for observed neural responses, such as the response properties of neurons in the posterior parietal cortex68 and primary motor cortex69. | |
C8565 | Predictive modeling is the process of using known results to create, process, and validate a model that can be used to forecast future outcomes. It is a tool used in predictive analytics, a data mining technique that attempts to answer the question "what might possibly happen in the future?" | |
C8566 | Continuous variables are numeric variables that have an infinite number of values between any two values. A continuous variable can be numeric or date/time. For example, the length of a part or the date and time a payment is received. | |
C8567 | Conditional Random Fields (CRF) CRF is a discriminant model for sequences data similar to MEMM. It models the dependency between each state and the entire input sequences. Unlike MEMM, CRF overcomes the label bias issue by using global normalizer. | |
C8568 | Experimental probability is the actual result of an experiment, which may be different from the theoretical probability. Example: you conduct an experiment where you flip a coin 100 times. The theoretical probability is 50% heads, 50% tails. The actual outcome of your experiment may be 47 heads, 53 tails. | |
C8569 | Artificial Intelligence is the broader concept of machines being able to carry out tasks in a way that we would consider “smart”. And, Machine Learning is a current application of AI based around the idea that we should really just be able to give machines access to data and let them learn for themselves. | |
C8570 | The T distribution is similar to the normal distribution, just with fatter tails. Both assume a normally distributed population. T distributions have higher kurtosis than normal distributions. The probability of getting values very far from the mean is larger with a T distribution than a normal distribution. | |
C8571 | Examples of dependence without correlation are uncorrelated. are not independent. | |
C8572 | Loss functions in neural networks The loss function is what SGD is attempting to minimize by iteratively updating the weights in the network. At the end of each epoch during the training process, the loss will be calculated using the network's output predictions and the true labels for the respective input. | |
C8573 | Second quartile (Q2) which is more commonly known as median splits the data in half (50%). Median divides the data into a lower half and an upper half. Third quartile (Q3), also known as upper quartile, splits lowest 75% (or highest 25%) of data. It is the middle value of the upper half. | |
C8574 | The Fourier amplitude spectrum of strong earthquake acceleration is one of the most direct and common. functions used to describe the frequency content of strong earthquake shaking.' It is used in source. mechanism studies, where its amplitudes and the parameters describing its shape can be related to the slip on. | |
C8575 | The 1×1 filter can be used to create a linear projection of a stack of feature maps. The projection created by a 1×1 can act like channel-wise pooling and be used for dimensionality reduction. The projection created by a 1×1 can also be used directly or be used to increase the number of feature maps in a model. | |
C8576 | Binning or discretization is the process of transforming numerical variables into categorical counterparts. An example is to bin values for Age into categories such as 20-39, 40-59, and 60-79. Numerical variables are usually discretized in the modeling methods based on frequency tables (e.g., decision trees). | |
C8577 | A traditional default value for the learning rate is 0.1 or 0.01, and this may represent a good starting point on your problem. — Practical recommendations for gradient-based training of deep architectures, 2012. | |
C8578 | The universality property of Turing machines states that there exists a Turing machine, which can simulate the behaviour of any other Turing machine. It says that a Turing machine can be adapted to different tasks by programming; from the viewpoint of computability it is not necessary to build special-purpose machines. | |
C8579 | Let's establish a very basic fact, one of the simplest methods for calculating the correctness of a model is to use the error between predicted value and actual value.The metrics we want to look at are:Mean Absolute Error (MAE)Root Mean Squared Error (RMSE)Mean Absolute Percentage Error (MAPE)R-Squared Score. | |
C8580 | Handling overfittingReduce the network's capacity by removing layers or reducing the number of elements in the hidden layers.Apply regularization , which comes down to adding a cost to the loss function for large weights.Use Dropout layers, which will randomly remove certain features by setting them to zero. | |
C8581 | Reward functions describe how the agent "ought" to behave. In other words, they have "normative" content, stipulating what you want the agent to accomplish. For example, some rewarding state s might represent the taste of food. Or perhaps, (s,a) might represent the act of tasting the food. | |
C8582 | A z-score measures exactly how many standard deviations above or below the mean a data point is. A negative z-score says the data point is below average. A z-score close to 0 says the data point is close to average. A data point can be considered unusual if its z-score is above 3 or below −3 . | |
C8583 | FDR is a very simple concept. It is the number of false discoveries in an experiment divided by total number of discoveries in that experiment. (You calculate one P-value for each sample or test in your experiment.) | |
C8584 | Goodness-of-fit tests are almost always right-tailed. This is because if, say, the observed frequencies were exactly the same as the expected, would be always zero, as would and . The more different the observed frequencies are from the expected, the bigger the . | |
C8585 | The F-value is the test statistic used to determine whether the term is associated with the response. F-value for the lack-of-fit test. The F-value is the test statistic used to determine whether the model is missing higher-order terms that include the predictors in the current model. | |
C8586 | A Bayesian network is a directed graphical model. (A Markov random field is a undirected graphical model.) A graphical model captures the conditional independence, which can be different from the Markovian property. | |
C8587 | Probability is the chance of an event occurring. A probability distribution is a table or an equation that links each outcome of a statistical experiment with its probability of occurrence. | |
C8588 | Definition: Given data the maximum likelihood estimate (MLE) for the parameter p is the value of p that maximizes the likelihood P(data |p). That is, the MLE is the value of p for which the data is most likely. 100 P(55 heads|p) = ( 55 ) p55(1 − p)45. | |
C8589 | The variance estimated as the average squared difference from the sample mean will always be less than the variance estimated as the average squared difference from the population mean unless the sample mean equals the population mean in which case they will be the same. | |
C8590 | In the extended Kalman filter, the state transition and observation models don't need to be linear functions of the state but may instead be differentiable functions. These matrices can be used in the Kalman filter equations. This process essentially linearizes the non-linear function around the current estimate. | |
C8591 | How big data analytics worksdata mining, which sift through data sets in search of patterns and relationships;predictive analytics, which build models to forecast customer behavior and other future developments;machine learning, which taps algorithms to analyze large data sets; and.More items | |
C8592 | Batch normalization is a technique for training very deep neural networks that standardizes the inputs to a layer for each mini-batch. This has the effect of stabilizing the learning process and dramatically reducing the number of training epochs required to train deep networks. | |
C8593 | To me an intuitive explanation is that minimizing the log loss equals minimizing the Kullback-Leibler divergence (Kullback–Leibler divergence - Wikipedia ) between the function you want to optimize (for example a neural network) and the true function that generates the data (from which you have samples in the form of a | |
C8594 | Imputation is a term that denotes a procedure that replaces the missing values in a data set by some plausible values. Our anal- ysis indicates that missing data imputation based on the k-nearest neighbour algorithm can outperform the internal methods used by C4. 5 and CN2 to treat missing data. | |
C8595 | 2- Key characteristics of machine learning2.1- The ability to perform automated data visualization. 2.2- Automation at its best. 2.3- Customer engagement like never before. 2.4- The ability to take efficiency to the next level when merged with IoT. 2.5- The ability to change the mortgage market. 2.6- Accurate data analysis.More items | |
C8596 | In statistics, a type of probability distribution in which all outcomes are equally likely. A coin also has a uniform distribution because the probability of getting either heads or tails in a coin toss is the same. | |
C8597 | GPU: RTX 2070 or RTX 2080 Ti. GTX 1070, GTX 1080, GTX 1070 Ti, and GTX 1080 Ti from eBay are good too! CPU: 1-2 cores per GPU depending how you preprocess data. > 2GHz; CPU should support the number of GPUs that you want to run. | |
C8598 | A statistical model is autoregressive if it predicts future values based on past values. For example, an autoregressive model might seek to predict a stock's future prices based on its past performance. | |
C8599 | Image Processing or Digital Image Processing is technique to improve image quality by applying mathematical operations. Image Processing Projects involves modifying images by identification of its two dimensional signal and enhancing it by comparing with standard signal. |
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