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C10600 | Unlike the independent-samples t-test, the Mann-Whitney U test allows you to draw different conclusions about your data depending on the assumptions you make about your data's distribution. These different conclusions hinge on the shape of the distributions of your data, which we explain more about later. | |
C10601 | An autoregressive (AR) model predicts future behavior based on past behavior. It's used for forecasting when there is some correlation between values in a time series and the values that precede and succeed them. Where simple linear regression and AR models differ is that Y is dependent on X and previous values for Y. | |
C10602 | Implementing Stochastic Gradient Descent (SGD) with Python# import the necessary packages.import matplotlib.pyplot as plt.from sklearn.datasets.samples_generator import make_blobs.import numpy as np.import argparse.def sigmoid_activation(x):# compute and return the sigmoid activation value for a.# given input value.More items• | |
C10603 | BACKWARD STEPWISE REGRESSION is a stepwise regression approach that begins with a full (saturated) model and at each step gradually eliminates variables from the regression model to find a reduced model that best explains the data. Also known as Backward Elimination regression. | |
C10604 | The number of true positives is placed in the top left cell of the confusion matrix. The data rows (emails) belonging to the positive class (spam) and incorrectly classified as negative (normal emails). These are called False Negatives (FN). | |
C10605 | Heuristics are the "shortcuts" that humans use to reduce task complexity in judgment and choice, and biases are the resulting gaps between normative behavior and the heuristically determined behavior (Kahneman et al., 1982). | |
C10606 | To determine whether the correlation between variables is significant, compare the p-value to your significance level. Usually, a significance level (denoted as α or alpha) of 0.05 works well. An α of 0.05 indicates that the risk of concluding that a correlation exists—when, actually, no correlation exists—is 5%. | |
C10607 | The machine operates on an infinite memory tape divided into discrete "cells". The machine positions its "head" over a cell and "reads" or "scans" the symbol there. The Turing machine was invented in 1936 by Alan Turing, who called it an "a-machine" (automatic machine). | |
C10608 | There are many types of motors are available in today's market, but mostly Tiny pager motors, servo motors, linear motors, stepper motors and DC geared motors are used in industrial robots according to their application area. | |
C10609 | The probability of each value of the discrete random variable is between 0 and 1, inclusive, and the sum of all the probabilities is 1. It is the expected value of a discrete random variable. | |
C10610 | A good knowledge representation system must have properties such as: Representational Accuracy: It should represent all kinds of required knowledge. Inferential Adequacy: It should be able to manipulate the representational structures to produce new knowledge corresponding to the existing structure. | |
C10611 | Skewness refers to distortion or asymmetry in a symmetrical bell curve, or normal distribution, in a set of data. If the curve is shifted to the left or to the right, it is said to be skewed. Skewness can be quantified as a representation of the extent to which a given distribution varies from a normal distribution. | |
C10612 | A decision tree is a flowchart-like diagram that shows the various outcomes from a series of decisions. It can be used as a decision-making tool, for research analysis, or for planning strategy. A primary advantage for using a decision tree is that it is easy to follow and understand. | |
C10613 | Bimodal Distribution: Two Peaks. The bimodal distribution has two peaks. However, if you think about it, the peaks in any distribution are the most common number(s). The two peaks in a bimodal distribution also represent two local maximums; these are points where the data points stop increasing and start decreasing. | |
C10614 | Ridge Regression is a technique for analyzing multiple regression data that suffer from multicollinearity. When multicollinearity occurs, least squares estimates are unbiased, but their variances are large so they may be far from the true value. | |
C10615 | Machine learning, on the other hand, refers to a group of techniques used by data scientists that allow computers to learn from data. These techniques produce results that perform well without programming explicit rules. Although data science includes machine learning, it is a vast field with many different tools. | |
C10616 | 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 setting a learning rate, there is a trade-off between the rate of convergence and overshooting. | |
C10617 | Symbolic artificial intelligence is the term for the collection of all methods in artificial intelligence research that are based on high-level "symbolic" (human-readable) representations of problems, logic and search. Production rules connect symbols in a relationship similar to an If-Then statement. | |
C10618 | Natural Language Processing (NLP) is the sub-branch of Data Science that attempts to extract insights from “text.” Thus, NLP is assuming an important role in Data Science. | |
C10619 | 5 Most Important Methods For Statistical Data AnalysisMean. The arithmetic mean, more commonly known as “the average,” is the sum of a list of numbers divided by the number of items on the list. Standard Deviation. Regression. Sample Size Determination. Hypothesis Testing. | |
C10620 | Regularized regression is a type of regression where the coefficient estimates are constrained to zero. The magnitude (size) of coefficients, as well as the magnitude of the error term, are penalized. Complex models are discouraged, primarily to avoid overfitting. | |
C10621 | Multicollinearity is a problem because it undermines the statistical significance of an independent variable. Other things being equal, the larger the standard error of a regression coefficient, the less likely it is that this coefficient will be statistically significant. | |
C10622 | If you are already a programmer and has basic knowledge of how it works. I would say 2 days to a month to learn it. Toby Thain, Started at around 10 years old. Still learning. | |
C10623 | An important way of checking whether a regression, simple or multiple, has achieved its goal to explain as much variation as possible in a dependent variable while respecting the underlying assumption, is to check the residuals of a regression. If groups of observations were overlooked, they'll show up in the residuals. | |
C10624 | In machine learning, a hyperparameter is a parameter whose value is used to control the learning process. By contrast, the values of other parameters (typically node weights) are derived via training. Given these hyperparameters, the training algorithm learns the parameters from the data. | |
C10625 | 1 Answer. In fact for a sample space containing 2 possible outcomes Ω={a,b}, the event space contains 4 events, F={a,b,ab,∅}. In general, for a sample space containing n possible outcomes, the event space is the power set of the sample space, so contains 2n events. | |
C10626 | Recurrent neural networks, or RNNs, are a type of artificial neural network that add additional weights to the network to create cycles in the network graph in an effort to maintain an internal state. | |
C10627 | A stationary time series is one whose properties do not depend on the time at which the series is observed. 14. Thus, time series with trends, or with seasonality, are not stationary — the trend and seasonality will affect the value of the time series at different times. | |
C10628 | Communication TheoriesActor-Network Theory (ANT) Adaptive Structuration Theory (AST) Agenda Setting Theory. Cognitive Dissonance Theory. Groupthink. Priming. Social Exchange Theory. Social Learning Theory.More items | |
C10629 | Stationarity. A common assumption in many time series techniques is that the data are stationary. A stationary process has the property that the mean, variance and autocorrelation structure do not change over time. | |
C10630 | The year is a categorical variable. The ratio between two years is not meaningful which is why its not appropriate to classify it as a quantitative variable. | |
C10631 | A finite population is a collection of objects or individuals that are objects of research that occupy a certain area. It clear boundaries that distinguish these population groups from other populations. | |
C10632 | To predict a continuous value, you need to adjust your model (regardless whether it is Recurrent or Not) to the following conditions:Use a linear activation function for the final layer.Chose an appropriate cost function (square error loss is typically used to measure the error of predicting real values) | |
C10633 | Blended learning, also known as b-Learning, is a combination of offline and online instruction where students interact with the instructor, the material, and other students through both a physical classroom and an online platform. | |
C10634 | Generalization is a term used to describe a model's ability to react to new data. Generalization is the ability of your model, after being trained to digest new data and make accurate predictions. | |
C10635 | The law of averages is a lay term used to express a belief that outcomes of a random event will “even out” within a small sample. The law of averages says it's due to land on black! ” Of course, the wheel has no memory and its probabilities do not change according to past results. | |
C10636 | Federated learning (also known as collaborative learning) is a machine learning technique that trains an algorithm across multiple decentralized edge devices or servers holding local data samples, without exchanging them. | |
C10637 | A 100MP smartphone camera will likely have pixels that are too small for even pixel-binning to make a big difference. As we know it today, pixel-binning might not be able to produce great results with a 100MP camera, as the pixels could be far too small. Both of these devices offer a 12MP 1.4-micron pixel main camera. | |
C10638 | 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. | |
C10639 | Descriptive statistics summarize the characteristics of a data set. Inferential statistics allow you to test a hypothesis or assess whether your data is generalizable to the broader population. | |
C10640 | The first step in backward elimination is pretty simple, you just select a significance level, or select the P-value. Usually, in most cases, a 5% significance level is selected. This means the P-value will be 0.05. You can change this value depending on the project. | |
C10641 | The normal curve is called Mesokurtic curve. If the curve of a distribution is peaked than a normal or mesokurtic curve then it is referred to as a Leptokurtic curve. If a curve is less peaked than a normal curve, it is called as a Platykurtic curve. That's why kurtosis of normal distribution equal to three. | |
C10642 | A population is the entire group that you want to draw conclusions about. A sample is the specific group that you will collect data from. The size of the sample is always less than the total size of the population. | |
C10643 | Depending on how the machine learning systems are used, such biases could result in lower customer service experiences, reduced sales and revenue, unfair or possibly illegal actions, and potentially dangerous conditions. | |
C10644 | The first step in backward elimination is pretty simple, you just select a significance level, or select the P-value. Usually, in most cases, a 5% significance level is selected. This means the P-value will be 0.05. You can change this value depending on the project. | |
C10645 | You might also see this written as something like “An unbiased estimator is when the mean of the statistic's sampling distribution is equal to the population's parameter.” This essentially means the same thing: if the statistic equals the parameter, then it's unbiased. | |
C10646 | A non-correlated asset is exactly what sounds like: an asset whose value isn't tied to larger fluctuations in the traditional markets. Yes, it's true that broad market movements can impact any asset, even those considered traditionally non-correlated. | |
C10647 | Fuzzy logic is useful for commercial and practical purposes. It can control machines and consumer products. It may not give accurate reasoning, but acceptable reasoning. Fuzzy logic helps to deal with the uncertainty in engineering. | |
C10648 | A control group is a set of experimental samples or subjects that are kept separate and aren't exposed to the independent variable. A controlled experiment is one in which every parameter is held constant except for the experimental (independent) variable. | |
C10649 | “The benefit to using a one-tailed test is that it requires fewer subjects to reach significance. A two-tailed test splits your significance level and applies it in both directions. Thus, each direction is only half as strong as a one-tailed test, which puts all the significance in one direction. | |
C10650 | With cluster sampling, in contrast, the sample includes elements only from sampled clusters. Multistage sampling. With multistage sampling, we select a sample by using combinations of different sampling methods. For example, in Stage 1, we might use cluster sampling to choose clusters from a population. | |
C10651 | Coefficients of linear discriminants: Shows the linear combination of predictor variables that are used to form the LDA decision rule. for example, LD1 = 0.91*Sepal. | |
C10652 | Word2vec is a group of related models that are used to produce word embeddings. Word2vec takes as its input a large corpus of text and produces a vector space, typically of several hundred dimensions, with each unique word in the corpus being assigned a corresponding vector in the space. | |
C10653 | An endogenous variable is a variable in a statistical model that's changed or determined by its relationship with other variables within the model. In other words, an endogenous variable is synonymous with a dependent variable, meaning it correlates with other factors within the system being studied. | |
C10654 | A machine learning pipeline is used to help automate machine learning workflows. They operate by enabling a sequence of data to be transformed and correlated together in a model that can be tested and evaluated to achieve an outcome, whether positive or negative. | |
C10655 | Than sentence examplesHe thinks you are better than us. He has lived more than eighty years. Alex had been hiding more than a father. Less than a week later she passed another milestone. No one could have been more private than Josh. "That's all right," returned the man's voice, more pleasantly than before.More items | |
C10656 | Random error is always present in a measurement. It is caused by inherently unpredictable fluctuations in the readings of a measurement apparatus or in the experimenter's interpretation of the instrumental reading. They can be estimated by comparing multiple measurements, and reduced by averaging multiple measurements. | |
C10657 | : being, relating to, or involving statistical methods that assign probabilities or distributions to events (such as rain tomorrow) or parameters (such as a population mean) based on experience or best guesses before experimentation and data collection and that apply Bayes' theorem to revise the probabilities and | |
C10658 | Calculation. The formula given in most textbooks is Skew = 3 * (Mean – Median) / Standard Deviation. This is known as an alternative Pearson Mode Skewness. You could calculate skew by hand. | |
C10659 | Tokenization is the process of tokenizing or splitting a string, text into a list of tokens. One can think of token as parts like a word is a token in a sentence, and a sentence is a token in a paragraph. How sent_tokenize works ? The sent_tokenize function uses an instance of PunktSentenceTokenizer from the nltk. | |
C10660 | On a technical note, estimation of a latent variable is done by analyzing the variance and covariance of the indicators. The measurement model of a latent variable with effect indicators is the set of relationships (modeled as equations) in which the latent variable is set as the predictor of the indicators. | |
C10661 | Consider these five ways to promote active learning in your own classroom.Make Videos and Photographs Engaging. They say a picture is worth 1,000 words. Have to Lecture? Keep It Interactive. Incorporate Games and Puzzles. Harness the Power of Social Collaboration. Assign Flexible Projects. | |
C10662 | There are two sets of degrees of freedom; one for the numerator and one for the denominator. For example, if F follows an F distribution and the number of degrees of freedom for the numerator is four, and the number of degrees of freedom for the denominator is ten, then F ~ F 4,10. | |
C10663 | Predictive models use known results to develop (or train) a model that can be used to predict values for different or new data. The modeling results in predictions that represent a probability of the target variable (for example, revenue) based on estimated significance from a set of input variables. | |
C10664 | Dependent events influence the probability of other events – or their probability of occurring is affected by other events. Independent events do not affect one another and do not increase or decrease the probability of another event happening. | |
C10665 | A distribution is skewed if one of its tails is longer than the other. The first distribution shown has a positive skew. This means that it has a long tail in the positive direction. The distribution below it has a negative skew since it has a long tail in the negative direction. | |
C10666 | There are three main methods for handling continuous variables in naive Bayes classifiers, namely, the normal method (parametric approach), the kernel method (non parametric approach) and discretization. | |
C10667 | The expected value (i.e. the mean) of a uniform random variable X is: E(X) = (1/2) (a + b) This is also written equivalently as: E(X) = (b + a) / 2. “a” in the formula is the minimum value in the distribution, and “b” is the maximum value. | |
C10668 | The optimal K value usually found is the square root of N, where N is the total number of samples. Use an error plot or accuracy plot to find the most favorable K value. KNN performs well with multi-label classes, but you must be aware of the outliers. | |
C10669 | Generally, z-tests are used when we have large sample sizes (n > 30), whereas t-tests are most helpful with a smaller sample size (n < 30). Both methods assume a normal distribution of the data, but the z-tests are most useful when the standard deviation is known. | |
C10670 | Improving the PF can maximize current-carrying capacity, improve voltage to equipment, reduce power losses, and lower electric bills. The simplest way to improve power factor is to add PF correction capacitors to the electrical system. PF correction capacitors act as reactive current generators. | |
C10671 | four | |
C10672 | The probability distribution of a discrete random variable can always be represented by a table. For example, suppose you flip a coin two times. The probability of getting 0 heads is 0.25; 1 head, 0.50; and 2 heads, 0.25. Thus, the table is an example of a probability distribution for a discrete random variable. | |
C10673 | Exploding gradients are a problem where large error gradients accumulate and result in very large updates to neural network model weights during training. This has the effect of your model being unstable and unable to learn from your training data. | |
C10674 | In the ARCH(q) process the conditional variance is specified as a linear function of past sample variances only, whereas the GARCH(p, q) process allows lagged conditional variances to enter as well. This corresponds to some sort of adaptive learning mechanism. | |
C10675 | For example, a network with two variables in the input layer, one hidden layer with eight nodes, and an output layer with one node would be described using the notation: 2/8/1. I recommend using this notation when describing the layers and their size for a Multilayer Perceptron neural network. | |
C10676 | Random samples are the best method of selecting your sample from the population of interest. The advantages are that your sample should represent the target population and eliminate sampling bias. The disadvantage is that it is very difficult to achieve (i.e. time, effort and money). | |
C10677 | In statistics, the coefficient of determination, denoted R2 or r2 and pronounced "R squared", is the proportion of the variance in the dependent variable that is predictable from the independent variable(s). | |
C10678 | In probability theory and statistics, the Poisson distribution (/ˈpwɑːsɒn/; French pronunciation: [pwasɔ̃]), named after French mathematician Siméon Denis Poisson, is a discrete probability distribution that expresses the probability of a given number of events occurring in a fixed interval of time or space if these | |
C10679 | The time spent waiting between events is often modeled using the exponential distribution. For example, suppose that an average of 30 customers per hour arrive at a store and the time between arrivals is exponentially distributed. | |
C10680 | The correlation coefficient is a statistical measure of the strength of the relationship between the relative movements of two variables. The values range between -1.0 and 1.0. Since oil companies earn greater profits as oil prices rise, the correlation between the two variables is highly positive. | |
C10681 | Instance-based methods are sometimes referred to as lazy learning methods because they delay processing until a new instance must be classified. The nearest neighbors of an instance are defined in terms of Euclidean distance. | |
C10682 | AUC represents the probability that a random positive (green) example is positioned to the right of a random negative (red) example. AUC ranges in value from 0 to 1. A model whose predictions are 100% wrong has an AUC of 0.0; one whose predictions are 100% correct has an AUC of 1.0. AUC is scale-invariant. | |
C10683 | Stratified random sampling is one common method that is used by researchers because it enables them to obtain a sample population that best represents the entire population being studied, making sure that each subgroup of interest is represented. All the same, this method of research is not without its disadvantages. | |
C10684 | Particle filtering uses a set of particles (also called samples) to represent the posterior distribution of some stochastic process given noisy and/or partial observations. The state-space model can be nonlinear and the initial state and noise distributions can take any form required. | |
C10685 | Normal distribution, also known as the Gaussian distribution, is a probability distribution that is symmetric about the mean, showing that data near the mean are more frequent in occurrence than data far from the mean. In graph form, normal distribution will appear as a bell curve. | |
C10686 | 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. | |
C10687 | The SD is usually more useful to describe the variability of the data while the variance is usually much more useful mathematically. For example, the sum of uncorrelated distributions (random variables) also has a variance that is the sum of the variances of those distributions. | |
C10688 | Use of AI in Following Things/Fields/Areas:Virtual Assistant or Chatbots.Agriculture and Farming.Autonomous Flying.Retail, Shopping and Fashion.Security and Surveillance.Sports Analytics and Activities.Manufacturing and Production.Live Stock and Inventory Management.More items• | |
C10689 | Greedy algorithms produce good solutions on some mathematical problems, but not on others. Most problems for which they work will have two properties: Greedy choice property. We can make whatever choice seems best at the moment and then solve the subproblems that arise later. | |
C10690 | A Power Spectral Density (PSD) is the measure of signal's power content versus frequency. Therefore, while the power spectrum calculates the area under the signal plot using the discrete Fourier Transform, the power spectrum density assigns units of power to each unit of frequency and thus, enhances periodicities. | |
C10691 | 0:041:23Suggested clip · 72 secondsQuick Example - Find the Area to the Right Of a Z-Score - YouTubeYouTubeStart of suggested clipEnd of suggested clip | |
C10692 | Take the sum of all the deviations (they should all be positive numbers because of the absolute value operation), then divide by the number of deviations you have added together. This result is the average deviation from the mean. | |
C10693 | Recommender systems are an important class of machine learning algorithms that offer "relevant" suggestions to users. Categorized as either collaborative filtering or a content-based system, check out how these approaches work along with implementations to follow from example code. | |
C10694 | The distinction between probability and likelihood is fundamentally important: Probability attaches to possible results; likelihood attaches to hypotheses. Explaining this distinction is the purpose of this first column. Possible results are mutually exclusive and exhaustive. | |
C10695 | The survival function is S(t) = Pr(T >t)=1 − F(t). – The survival function gives the probability that a subject will survive past time t. | |
C10696 | Best practices – Machine Learning models and applicationsIdentify the business problem and the right success metrics. Begin with it. Gather correct data. Move the algorithms instead of your data. Initiate tests before the actual launch. Avoid data dropping while machine learning algorithms train. Keep away from objectives that are unaligned. Keep using codes.More items• | |
C10697 | Jensen's inequality states that this line is everywhere at least as large as f(x). pf(x1) + (1 − p)f(x2) ≥ f(px1 + (1 − p)x2). If f is (doubly) differentiable then f is convex if and only if d2f/dx2 ≥ 0. Now consider a probability distribution P on a set M and a function X assigning real values X(m) for m ∈ M. | |
C10698 | As a rule of thumb, I'd say that SVMs are great for relatively small data sets with fewer outliers. Also, deep learning algorithms require much more experience: Setting up a neural network using deep learning algorithms is much more tedious than using an off-the-shelf classifiers such as random forests and SVMs. | |
C10699 | Backpropagation and computing gradients. According to the paper from 1989, backpropagation: repeatedly adjusts the weights of the connections in the network so as to minimize a measure of the difference between the actual output vector of the net and the desired output vector. |
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