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C400 | A moving average is a technique to get an overall idea of the trends in a data set; it is an average of any subset of numbers. The moving average is extremely useful for forecasting long-term trends. You can calculate it for any period of time. Moving averages are usually plotted and are best visualized. | |
C401 | Try a series of runs with different amounts of training data: randomly sample 20% of it, say, 10 times and observe performance on the validation data, then do the same with 40%, 60%, 80%. You should see both greater performance with more data, but also lower variance across the different random samples. | |
C402 | Logistic Regression, also known as Logit Regression or Logit Model, is a mathematical model used in statistics to estimate (guess) the probability of an event occurring having been given some previous data. Logistic Regression works with binary data, where either the event happens (1) or the event does not happen (0). | |
C403 | To understand potential interaction effects, compare the lines from the interaction plot: If the lines are parallel, there is no interaction. If the lines are not parallel, there is an interaction. | |
C404 | The root-mean-square deviation (RMSD) or root-mean-square error (RMSE) is a frequently used measure of the differences between values (sample or population values) predicted by a model or an estimator and the values observed. | |
C405 | Motivation is the reason for people's actions, willingness and goals. Motivation is derived from the word motive which is defined as a need that requires satisfaction. These needs could be wants or desires that are acquired through influence of culture, society, lifestyle, etc. or generally innate. | |
C406 | A person is faced with a stimulus that is very faint or confusing. If the signal is present the person can decide that it is present or absent. These outcomes are called hits and misses. If the signal is absent the person can still decide that the signal is either present or absent. | |
C407 | Train a neural network with TensorFlowStep 1: Import the data.Step 2: Transform the data.Step 3: Construct the tensor.Step 4: Build the model.Step 5: Train and evaluate the model.Step 6: Improve the model. | |
C408 | Assuming a double-blind test is not possible, here are some techniques that can help:Standardize everything: the research protocol, the moderator script, the questions etc. Have a second researcher monitor the first researcher. Stay out of the participant's line of sight. Practice.More items• | |
C409 | Standard deviation tells you how spread out the data is. It is a measure of how far each observed value is from the mean. In any distribution, about 95% of values will be within 2 standard deviations of the mean. | |
C410 | The false-positive rate is plotted on the x-axis and the true positive rate is plotted on the y-axis and the plot is referred to as the Receiver Operating Characteristic curve, or ROC curve. This would be a threshold on the curve that is closest to the top-left of the plot. | |
C411 | A recurrent neural network (RNN) is a type of neural network commonly used in speech recognition. RNNs are designed to recognize the sequential characteristics in data and use patterns to predict the next likely scenario. | |
C412 | A P.I Controller is a feedback control loop that calculates an error signal by taking the difference between the output of a system, which in this case is the power being drawn from the battery, and the set point. | |
C413 | Some of the practical applications of reinforcement learning are:Manufacturing. In Fanuc, a robot uses deep reinforcement learning to pick a device from one box and putting it in a container. Inventory Management. Delivery Management. Power Systems. Finance Sector. | |
C414 | We can call a Logistic Regression a Linear Regression model but the Logistic Regression uses a more complex cost function, this cost function can be defined as the 'Sigmoid function' or also known as the 'logistic function' instead of a linear function. | |
C415 | Discriminant function analysis (DFA) is a statistical procedure that classifies unknown individuals and the probability of their classification into a certain group (such as sex or ancestry group). Discriminant function analysis makes the assumption that the sample is normally distributed for the trait. | |
C416 | is that independence is the state or quality of being independent; freedom from dependence; exemption from reliance on, or control by others; self-subsistence or maintenance; direction of one's own affairs without interference while independent is a candidate or voter not affiliated with any political party, a free | |
C417 | Blaise Pascal | |
C418 | A mutually exclusive pair of events are complements to each other. For example: If the desired outcome is heads on a flipped coin, the complement is tails. The Complement Rule states that the sum of the probabilities of an event and its complement must equal 1, or for the event A, P(A) + P(A') = 1. | |
C419 | The coefficient of variation represents the ratio of the standard deviation to the mean, and it is a useful statistic for comparing the degree of variation from one data series to another, even if the means are drastically different from one another. | |
C420 | An activation function is a function used in artificial neural networks which outputs a small value for small inputs, and a larger value if its inputs exceed a threshold. If the inputs are large enough, the activation function "fires", otherwise it does nothing. | |
C421 | Given these assumptions, we know the following.The expected value of the difference between all possible sample means is equal to the difference between population means. Thus, The standard deviation of the difference between sample means (σd) is approximately equal to: σd = sqrt( σ12 / n1 + σ22 / n2 ) | |
C422 | ), while other elements may be complex. Hermitian matrices have real eigenvalues whose eigenvectors form a unitary basis. For real matrices, Hermitian is the same as symmetric. | |
C423 | In information retrieval, a perfect precision score of 1.0 means that every result retrieved by a search was relevant (but says nothing about whether all relevant documents were retrieved) whereas a perfect recall score of 1.0 means that all relevant documents were retrieved by the search (but says nothing about how | |
C424 | One advantage of decision tree-based methods like random forests is their ability to natively handle categorical predictors without having to first transform them (e.g., by using feature engineering techniques). | |
C425 | Definition. Predictive analytics is an area of statistics that deals with extracting information from data and using it to predict trends and behavior patterns. Predictive analytics statistical techniques include data modeling, machine learning, AI, deep learning algorithms and data mining. | |
C426 | In Supervised learning, you train the machine using data which is well "labeled." For example, Baby can identify other dogs based on past supervised learning. Regression and Classification are two types of supervised machine learning techniques. Clustering and Association are two types of Unsupervised learning. | |
C427 | The Bayesian approach permits the use of objective data or subjective opinion in specifying a prior distribution. With the Bayesian approach, different individuals might specify different prior distributions. Bayesian methods have been used extensively in statistical decision theory (see statistics: Decision analysis). | |
C428 | The p-value is calculated using the sampling distribution of the test statistic under the null hypothesis, the sample data, and the type of test being done (lower-tailed test, upper-tailed test, or two-sided test). an upper-tailed test is specified by: p-value = P(TS ts | H 0 is true) = 1 - cdf(ts) | |
C429 | In nonhierarchical clustering, such as the k-means algorithm, the relationship between clusters is undetermined. Hierarchical clustering repeatedly links pairs of clusters until every data object is included in the hierarchy. | |
C430 | First, it is a very quick estimate of the standard deviation. The standard deviation requires us to first find the mean, then subtract this mean from each data point, square the differences, add these, divide by one less than the number of data points, then (finally) take the square root. | |
C431 | 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. | |
C432 | A CNN has multiple layers. Weight sharing happens across the receptive field of the neurons(filters) in a particular layer. Weights are the numbers within each filter. These filters act on a certain receptive field/ small section of the image. When the filter moves through the image, the filter does not change. | |
C433 | Variance | |
C434 | Rather than trying to define a number, instead define what a field of numbers is; instead of defining what a vector is, consider instead all the vectors that make up a vector space. So to understand tensors of a particular type, instead consider all those tensors of the same type together. | |
C435 | Multicollinearity causes the following two basic types of problems: The coefficient estimates can swing wildly based on which other independent variables are in the model. Multicollinearity reduces the precision of the estimate coefficients, which weakens the statistical power of your regression model. | |
C436 | From a practical standpoint, L1 tends to shrink coefficients to zero whereas L2 tends to shrink coefficients evenly. L1 is therefore useful for feature selection, as we can drop any variables associated with coefficients that go to zero. L2, on the other hand, is useful when you have collinear/codependent features. | |
C437 | Homoskedastic (also spelled "homoscedastic") refers to a condition in which the variance of the residual, or error term, in a regression model is constant. That is, the error term does not vary much as the value of the predictor variable changes. | |
C438 | Detection theory or signal detection theory is a means to measure the ability to differentiate between information-bearing patterns (called stimulus in living organisms, signal in machines) and random patterns that distract from the information (called noise, consisting of background stimuli and random activity of the | |
C439 | The definition of an ensemble is two or more people or things that function together as a whole. An example of an ensemble is a string quartet. An example of an ensemble is a group of actors in a play. A small group of musicians playing or singing together. | |
C440 | The log transformation can be used to make highly skewed distributions less skewed. This can be valuable both for making patterns in the data more interpretable and for helping to meet the assumptions of inferential statistics. Figure 1 shows an example of how a log transformation can make patterns more visible. | |
C441 | Cross-validation is usually the preferred method because it gives your model the opportunity to train on multiple train-test splits. This gives you a better indication of how well your model will perform on unseen data. That makes the hold-out method score dependent on how the data is split into train and test sets. | |
C442 | Properties. The normal distribution is the only distribution whose cumulants beyond the first two (i.e., other than the mean and variance) are zero. It is also the continuous distribution with the maximum entropy for a specified mean and variance. | |
C443 | “Genetic Algorithms are good at taking large, potentially huge search spaces and navigating them, looking for optimal combinations of things, solutions you might not otherwise find in a lifetime.” The Genetic Algorithm (cont.) | |
C444 | The Gini index, or Gini coefficient, is a measure of the distribution of income across a population developed by the Italian statistician Corrado Gini in 1912. The coefficient ranges from 0 (or 0%) to 1 (or 100%), with 0 representing perfect equality and 1 representing perfect inequality. | |
C445 | Logistic regression is a classification algorithm, don't confuse with the name regression. | |
C446 | Data mining and machine learning are both rooted in data science. But there are several key distinctions between these two areas.Applications.Data miningMachine learningRecognizes patternsRecognizes patterns and adapts its analysis to the changing data sets2 more rows | |
C447 | An encoder is a network (FC, CNN, RNN, etc) that takes the input, and output a feature map/vector/tensor. An encoder is a network (FC, CNN, RNN, etc) that takes the input, and output a feature map/vector/tensor. These feature vector hold the information, the features, that represents the input. | |
C448 | The obvious difference between ANOVA and ANCOVA is the the letter "C", which stands for 'covariance'. Like ANOVA, "Analysis of Covariance" (ANCOVA) has a single continuous response variable. ANCOVA is also commonly used to describe analyses with a single response variable, continuous IVs, and no factors. | |
C449 | The images shows that a higher VC dimension allows for a lower empirical risk (the error a model makes on the sample data), but also introduces a higher confidence interval. This interval can be seen as the confidence in the model's ability to generalize. | |
C450 | Data Wrangling: Preparation of data during the interactive data analysis and model building. Typically done by a data scientist or business analyst to change views on a dataset and for features engineering. | |
C451 | When using the single sampling plan by attributes, one sample of size n is taken from the lot of size N and inspected. If there are c or less defective items in the sample, the lot is accepted. If there are more than c defective items in the sample, the lot is rejected. | |
C452 | Connectionism, an approach to artificial intelligence (AI) that developed out of attempts to understand how the human brain works at the neural level and, in particular, how people learn and remember. (For that reason, this approach is sometimes referred to as neuronlike computing.) | |
C453 | The logistic function is the inverse of the natural logit function and so can be used to convert the logarithm of odds into a probability. In mathematical notation the logistic function is sometimes written as expit in the same form as logit. | |
C454 | Data labeling, in the context of machine learning, is the process of detecting and tagging data samples. The process can be manual but is usually performed or assisted by software. | |
C455 | 1 Answer. In order to come up with a split point, the values are sorted, and the mid-points between adjacent values are evaluated in terms of some metric, usually information gain or gini impurity. For your example, lets say we have four examples and the values of the age variable are (20,29,40,50). | |
C456 | Sequential minimal optimization (SMO) is an algorithm for solving the quadratic programming (QP) problem that arises during the training of support-vector machines (SVM). SMO is widely used for training support vector machines and is implemented by the popular LIBSVM tool. | |
C457 | The root-mean-square deviation (RMSD) or root-mean-square error (RMSE) is a frequently used measure of the differences between values (sample or population values) predicted by a model or an estimator and the values observed. RMSD is the square root of the average of squared errors. | |
C458 | So, For hidden layers the best option to use is ReLU, and the second option you can use as SIGMOID. For output layers the best option depends, so we use LINEAR FUNCTIONS for regression type of output layers and SOFTMAX for multi-class classification. | |
C459 | With supervised learning, you have features and labels. The features are the descriptive attributes, and the label is what you're attempting to predict or forecast. Thus, for training the machine learning classifier, the features are customer attributes, the label is the premium associated with those attributes. | |
C460 | Just like the post period dummy variable controls for factors changing over time that are common to both treatment and control groups, the year fixed effects (i.e. year dummy variables) control for factors changing each year that are common to all cities for a given year. | |
C461 | A random variable is a variable whose value is a numerical outcome of a random phenomenon. A discrete random variable X has a countable number of possible values. Example: Let X represent the sum of two dice. A continuous random variable X takes all values in a given interval of numbers. | |
C462 | Formally, the p-value is the probability of seeing a particular result (or greater one) from zero, assuming that the null hypothesis is true. If “null hypothesis is true” is confusing, replace it with, “assuming we had really run an A/A test.” | |
C463 | Consequences of Heteroscedasticity The OLS estimators and regression predictions based on them remains unbiased and consistent. The OLS estimators are no longer the BLUE (Best Linear Unbiased Estimators) because they are no longer efficient, so the regression predictions will be inefficient too. | |
C464 | Predictor variables in the machine learning context the the input data or the variables that is mapped to the target variable through an empirical relation ship usually determined through the data. In statistics you you refer to them as predictors. Each set of predictors may be called as an observation. | |
C465 | The addition law of probability (sometimes referred to as the addition rule or sum rule), states that the probability that A or B will occur is the sum of the probabilities that A will happen and that B will happen, minus the probability that both A and B will happen. | |
C466 | Descriptive statistics uses the data to provide descriptions of the population, either through numerical calculations or graphs or tables. Inferential statistics makes inferences and predictions about a population based on a sample of data taken from the population in question. | |
C467 | Within an artificial neural network, a neuron is a mathematical function that model the functioning of a biological neuron. Typically, a neuron compute the weighted average of its input, and this sum is passed through a nonlinear function, often called activation function, such as the sigmoid. | |
C468 | Difference between Z score vs T score. Z score is a conversion of raw data to a standard score, when the conversion is based on the population mean and population standard deviation. T score is a conversion of raw data to the standard score when the conversion is based on the sample mean and sample standard deviation. | |
C469 | Clustering is useful for exploring data. If there are many cases and no obvious groupings, clustering algorithms can be used to find natural groupings. Clustering can also serve as a useful data-preprocessing step to identify homogeneous groups on which to build supervised models. | |
C470 | A t-test tests a null hypothesis about two means; most often, it tests the hypothesis that two means are equal, or that the difference between them is zero. A chi-square test tests a null hypothesis about the relationship between two variables. | |
C471 | Predictor variable and independent variable are both similar in that they are used to observe how they affect some other variable or outcome. The main difference is that independent variables can be used to determine if one variable is the cause of changes in another, whereas predictor variables cannot. | |
C472 | Residual analysis is used to assess the appropriateness of a linear regression model by defining residuals and examining the residual plot graphs. | |
C473 | There are two different ways to encoding categorical variables. One-hot encoding converts it into n variables, while dummy encoding converts it into n-1 variables. If we have k categorical variables, each of which has n values. | |
C474 | Each row of the matrix represents the instances in a predicted class while each column represents the instances in an actual class (or vice versa). The name stems from the fact that it makes it easy to see if the system is confusing two classes (i.e. commonly mislabeling one as another). | |
C475 | P-value = the sum of the probabilities for all tables having a probability equal to or smaller than that observed. Page 11. Fisher's exact test: the example. ∑log(nij!) | |
C476 | Autocorrelation represents the degree of similarity between a given time series and a lagged version of itself over successive time intervals. Autocorrelation measures the relationship between a variable's current value and its past values. | |
C477 | Sanderson points out in her book Social Psychology, confirmation bias also helps form and re-confirm stereotypes we have about people:3 "We also ignore information that disputes our expectations. | |
C478 | In statistics, the generalized linear model (GLM) is a flexible generalization of ordinary linear regression that allows for response variables that have error distribution models other than a normal distribution. | |
C479 | The square of the correlation coefficient, r², is a useful value in linear regression. This value represents the fraction of the variation in one variable that may be explained by the other variable. The correlation coefficient also relates directly to the regression line Y = a + bX for any two variables, where . | |
C480 | The Finite Population Correction Factor (FPC) is used when you sample without replacement from more than 5% of a finite population. It's needed because under these circumstances, the Central Limit Theorem doesn't hold and the standard error of the estimate (e.g. the mean or proportion) will be too big. | |
C481 | Simple linear regression is a regression model that estimates the relationship between one independent variable and one dependent variable using a straight line. Both variables should be quantitative. | |
C482 | ABSTRACT. We propose a practical method for L0 norm regularization for neural networks: pruning the network during training by encouraging weights to become exactly zero. Such regularization is interesting since (1) it can greatly speed up training and inference, and (2) it can improve generalization. | |
C483 | Discrete control systems, as considered here, refer to the control theory of discrete‐time Lagrangian or Hamiltonian systems. Geometric integrators are numericalintegration methods that preserve geometric properties of continuous systems, such as conservation of the symplectic form, momentum, and energy. | |
C484 | Kappa is widely used on Twitch in chats to signal you are being sarcastic or ironic, are trolling, or otherwise playing around with someone. It is usually typed at the end of a string of text, but, as can often the case on Twitch, it is also often used on its own or repeatedly (to spam someone). | |
C485 | It is linear if there exists a function H(x) = β0 + βT x such that h(x) = I(H(x) > 0). H(x) is also called a linear discriminant function. The decision boundary is therefore defined as the set {x ∈ Rd : H(x)=0}, which corresponds to a (d − 1)-dimensional hyperplane within the d-dimensional input space X. | |
C486 | Streaming Data is data that is generated continuously by thousands of data sources, which typically send in the data records simultaneously, and in small sizes (order of Kilobytes). | |
C487 | Let me put some light on the key challenges that appear while processing the data.9- SecurityMost of the data processing systems have a single level of protection.No encryption of Either the raw data or the result/ output data.Access of the data to unethical IT professional that risks in data loss. | |
C488 | This clustering method classifies the information into multiple groups based on the characteristics and similarity of the data. There are many algorithms that come under partitioning method some of the popular ones are K-Mean, PAM(K-Mediods), CLARA algorithm (Clustering Large Applications) etc. | |
C489 | Structured data is highly specific and is stored in a predefined format, where unstructured data is a conglomeration of many varied types of data that are stored in their native formats. This means that structured data takes advantage of schema-on-write and unstructured data employs schema-on-read. | |
C490 | GRU use less training parameters and therefore use less memory, execute faster and train faster than LSTM's whereas LSTM is more accurate on dataset using longer sequence. In short, if sequence is large or accuracy is very critical, please go for LSTM whereas for less memory consumption and faster operation go for GRU. | |
C491 | Probabilistic reasoning is a method of representation of knowledge where the concept of probability is applied to indicate the uncertainty in knowledge. Probabilistic reasoning is used in AI: When we are unsure of the predicates. When it is known that an error occurs during an experiment. | |
C492 | Bias in Machine Learning is defined as the phenomena of observing results that are systematically prejudiced due to faulty assumptions. This also results in bias which arises from the choice of training and test data and their representation of the true population. | |
C493 | Artificial intelligence is impacting the future of virtually every industry and every human being. Artificial intelligence has acted as the main driver of emerging technologies like big data, robotics and IoT, and it will continue to act as a technological innovator for the foreseeable future. | |
C494 | Definition. In machine learning, model validation is referred to as the process where a trained model is evaluated with a testing data set. The testing data set is a separate portion of the same data set from which the training set is derived. Model validation is carried out after model training. | |
C495 | When you get the features in lower dimensions then you will lose some information of data most of the times and you won't be able to interpret the lower dimension data. | |
C496 | Fine tuning is one approach to transfer learning, and it is very popular in computer vision and NLP. The most common example given is when a model is trained on ImageNet is fine-tuned on a second task. Transfer learning is when a model developed for one task is reused for a model on a second task. | |
C497 | Unlike Monte Carlo sampling methods that are able to draw independent samples from the distribution, Markov Chain Monte Carlo methods draw samples where the next sample is dependent on the existing sample, called a Markov Chain. | |
C498 | It is the simplest model to study polymers. In other fields of mathematics, random walk is used to calculate solutions to Laplace's equation, to estimate the harmonic measure, and for various constructions in analysis and combinatorics. In computer science, random walks are used to estimate the size of the Web. | |
C499 | Using proper validation techniques helps you understand your model, but most importantly, estimate an unbiased generalization performance.Splitting your data. k-Fold Cross-Validation (k-Fold CV) Leave-one-out Cross-Validation (LOOCV) Nested Cross-Validation. Time Series CV. Comparing Models. |
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