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C4400 | The most frequently used evaluation metric of survival models is the concordance index (c index, c statistic). It is a measure of rank correlation between predicted risk scores f^ and observed time points y that is closely related to Kendall's τ. | |
C4401 | The WordNet is a part of Python's Natural Language Toolkit. It is a large word database of English Nouns, Adjectives, Adverbs and Verbs. These are grouped into some set of cognitive synonyms, which are called synsets. In the wordnet, there are some groups of words, whose meaning are same. | |
C4402 | If the correlation between education and unobserved ability is positive, omitted variables bias will occur in an upward direction. Conversely, if the correlation between an explanatory variable and an unobserved relevant variable is negative, omitted variables bias will occur in a downward direction. | |
C4403 | The hypergeometric distribution is discrete. It is similar to the binomial distribution.The hypergeometric distribution is used under these conditions:Total number of items (population) is fixed.Sample size (number of trials) is a portion of the population.Probability of success changes after each trial. | |
C4404 | Below are the methods to convert a categorical (string) input to numerical nature:Label Encoder: It is used to transform non-numerical labels to numerical labels (or nominal categorical variables). Convert numeric bins to number: Let's say, bins of a continuous variable are available in the data set (shown below). | |
C4405 | Latent semantic indexing (LSI) is an indexing and retrieval method that uses a mathematical technique called singular value decomposition (SVD) to identify patterns in the relationships between the terms and concepts contained in an unstructured collection of text. | |
C4406 | Unsupervised machine learning helps you to finds all kind of unknown patterns in data. Clustering and Association are two types of Unsupervised learning. Four types of clustering methods are 1) Exclusive 2) Agglomerative 3) Overlapping 4) Probabilistic. | |
C4407 | Explanation: It is depth-first search algorithm because its space requirements are linear in the size of the proof. | |
C4408 | SVM Scoring Function A Support Vector Machine is a binary (two class) classifier; if the output of the scoring function is negative then the input is classified as belonging to class y = -1. If the score is positive, the input is classified as belonging to class y = 1. | |
C4409 | A GAN is a generative model that is trained using two neural network models. One model is called the “generator” or “generative network” model that learns to generate new plausible samples. After training, the generative model can then be used to create new plausible samples on demand. | |
C4410 | A pooling layer is another building block of a CNN. Its function is to progressively reduce the spatial size of the representation to reduce the amount of parameters and computation in the network. Pooling layer operates on each feature map independently. The most common approach used in pooling is max pooling. | |
C4411 | The basic principle behind the working of the boosting algorithm is to generate multiple weak learners and combine their predictions to form one strong rule. These weak rules are generated by applying base Machine Learning algorithms on different distributions of the data set. | |
C4412 | ROC curves are frequently used to show in a graphical way the connection/trade-off between clinical sensitivity and specificity for every possible cut-off for a test or a combination of tests. The term ROC stands for Receiver Operating Characteristic. | |
C4413 | numpy.random. permutation (x) Randomly permute a sequence, or return a permuted range. If x is a multi-dimensional array, it is only shuffled along its first index. | |
C4414 | Recall the relevant definitions. Two matrices A and B are similar if there exists a nonsingular (invertible) matrix S such […] If 2 by 2 Matrices Satisfy A=AB−BA, then A2 is Zero Matrix Let A,B be complex 2×2 matrices satisfying the relation A=AB−BA. Prove that A2=O, where O is the 2×2 zero matrix. | |
C4415 | Track a Single Object Using Kalman FilterCreate vision. KalmanFilter by using configureKalmanFilter.Use predict and correct methods in a sequence to eliminate noise present in the tracking system.Use predict method by itself to estimate ball's location when it is occluded by the box. | |
C4416 | Definition. Inter-rater reliability is the extent to which two or more raters (or observers, coders, examiners) agree. It addresses the issue of consistency of the implementation of a rating system. Inter-rater reliability can be evaluated by using a number of different statistics. | |
C4417 | Logistic regression is a pretty flexible method. It can readily use as independent variables categorical variables. Most software that use Logistic regression should let you use categorical variables. A single column in your model can handle as many categories as needed for a single categorical variable. | |
C4418 | A feature descriptor is an algorithm which takes an image and outputs feature descriptors/feature vectors. Feature descriptors encode interesting information into a series of numbers and act as a sort of numerical “fingerprint” that can be used to differentiate one feature from another. | |
C4419 | Low Pass filtering: It is also known as the smoothing filter. It removes the high-frequency content from the image. Median Filtering: It is also known as nonlinear filtering. It is used to eliminate salt and pepper noise. | |
C4420 | In sociology and social psychology, an in-group is a social group to which a person psychologically identifies as being a member. By contrast, an out-group is a social group with which an individual does not identify. | |
C4421 | Commonly used Statistical models in Predictive AnalyticsLogistic Regression: Logistic regression models the relation between a dependent and two or more independent variables (explanatory and response variables). Time Series: Clustering: Decision Trees: Neural Network: | |
C4422 | The main use of F-distribution is to test whether two independent samples have been drawn for the normal populations with the same variance, or if two independent estimates of the population variance are homogeneous or not, since it is often desirable to compare two variances rather than two averages. | |
C4423 | Two disjoint events can never be independent, except in the case that one of the events is null. Events are considered disjoint if they never occur at the same time. For example, being a freshman and being a sophomore would be considered disjoint events. Independent events are unrelated events. | |
C4424 | A vector is an object that has both a magnitude and a direction. Two examples of vectors are those that represent force and velocity. Both force and velocity are in a particular direction. The magnitude of the vector would indicate the strength of the force or the speed associated with the velocity. | |
C4425 | a graph marking the similarity or difference between two stimuli versus the similarity or difference in their elicited responses. See also stimulus generalization. | |
C4426 | Regularization Overview Regularization techniques address the prevention of ill-posed problems; problems where “the solution is highly sensitive to changes in the final data” (Wikipedia). Errors or problems with the data or method of inputting the data can lead to larger errors in the solutions. | |
C4427 | You can show that all states in the same communicating class have the same period. A class is said to be periodic if its states are periodic. Similarly, a class is said to be aperiodic if its states are aperiodic. Finally, a Markov chain is said to be aperiodic if all of its states are aperiodic. | |
C4428 | A typical perceptual map is a two-dimensional graph with a vertical axis (Y) and a horizontal axis (X). Each axis consists of a pair of opposite attributes at each end. | |
C4429 | 0:517:39Suggested clip · 113 secondsUnderstanding the normal distribution - statistics help - YouTubeYouTubeStart of suggested clipEnd of suggested clip | |
C4430 | Multivariate Normality–Multiple regression assumes that the residuals are normally distributed. No Multicollinearity—Multiple regression assumes that the independent variables are not highly correlated with each other. This assumption is tested using Variance Inflation Factor (VIF) values. | |
C4431 | Adjusted R-squared value can be calculated based on value of r-squared, number of independent variables (predictors), total sample size. Every time you add a independent variable to a model, the R-squared increases, even if the independent variable is insignificant. It never declines. | |
C4432 | Run regression analysisOn the Data tab, in the Analysis group, click the Data Analysis button.Select Regression and click OK.In the Regression dialog box, configure the following settings: Select the Input Y Range, which is your dependent variable. Click OK and observe the regression analysis output created by Excel. | |
C4433 | A quartile is a statistical term that describes a division of observations into four defined intervals based on the values of the data and how they compare to the entire set of observations. | |
C4434 | Events A and B are independent if the equation P(A∩B) = P(A) · P(B) holds true. You can use the equation to check if events are independent; multiply the probabilities of the two events together to see if they equal the probability of them both happening together. | |
C4435 | In artificial neural networks, the activation function of a node defines the output of that node given an input or set of inputs. A standard integrated circuit can be seen as a digital network of activation functions that can be "ON" (1) or "OFF" (0), depending on input. | |
C4436 | A dummy variable is a numerical variable used in regression analysis to represent subgroups of the sample in your study. In research design, a dummy variable is often used to distinguish different treatment groups. | |
C4437 | The normal distribution is a continuous probability distribution that is symmetrical on both sides of the mean, so the right side of the center is a mirror image of the left side. The normal distribution is often called the bell curve because the graph of its probability density looks like a bell. | |
C4438 | When resources are limited, populations exhibit logistic growth. In logistic growth, population expansion decreases as resources become scarce, leveling off when the carrying capacity of the environment is reached, resulting in an S-shaped curve. | |
C4439 | You can use test statistics to determine whether to reject the null hypothesis. The test statistic compares your data with what is expected under the null hypothesis. The test statistic is used to calculate the p-value. A test statistic measures the degree of agreement between a sample of data and the null hypothesis. | |
C4440 | When we refer to values as being “statistically equivalent” or to a “conclusion of statistical equivalence,” we mean the difference between groups is smaller than what is considered meaningful and statistically falls within the interval indicated by the equivalence bounds. In any one-sided test, for an alpha level of . | |
C4441 | Here are five ways, but it really all boils down to stretching your brain by learning new things:Become a renaissance man. Or woman. Play the brain game Dual N-Back. Do this 20 minutes a day. Do regular high cardio exercise. Learn an instrument. Buy the book Boost Your IQ by Carolyn Skitt, and play all the games. | |
C4442 | There are several methods through which you can evaluate a Logistic regression model:Goodness of Fit.Likelihood ratio test.Wald's Test.Hosmer-Lemeshov Test.ROC (AUC) curve.Confidence Intervals.Correlation factors and coefficients.Variance Inflation Factor(VIF)More items | |
C4443 | Cross-entropy can be calculated using the probabilities of the events from P and Q, as follows: H(P, Q) = – sum x in X P(x) * log(Q(x)) | |
C4444 | The planning in Artificial Intelligence is about the decision making tasks performed by the robots or computer programs to achieve a specific goal. The execution of planning is about choosing a sequence of actions with a high likelihood to complete the specific task. | |
C4445 | : 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 | |
C4446 | To put simply, likelihood is "the likelihood of θ having generated D" and posterior is essentially "the likelihood of θ having generated D" further multiplied by the prior distribution of θ. | |
C4447 | StepsStep 1: For each (x,y) point calculate x2 and xy.Step 2: Sum all x, y, x2 and xy, which gives us Σx, Σy, Σx2 and Σxy (Σ means "sum up")Step 3: Calculate Slope m:m = N Σ(xy) − Σx Σy N Σ(x2) − (Σx)2Step 4: Calculate Intercept b:b = Σy − m Σx N.Step 5: Assemble the equation of a line. | |
C4448 | Gradient boosted regression and classification is an additive training tree classification method where trees are build in series (iteratively) and compared to each other based on a mathematically derived score of splits. The trees are compared based on weighted leaf scores within each tree. | |
C4449 | To format the size of data points in a scatter plot graph, right click any of the data points and select 'format data series' then select marker options and customize for larger or smaller data points. | |
C4450 | Put more accurately, cross entropy error measures the difference between a correct probability distribution and a predicted distribution. Binary cross entropy can be calculated as above with no problem. Or suppose you have a different ML problem with correct = (1, 0) and predicted = (0.8, 0.2). | |
C4451 | Altman's Z-Score model is a numerical measurement that is used to predict the chances of a business going bankrupt in the next two years. The model was developed by American finance professor Edward Altman in 1968 as a measure of the financial stability of companies. | |
C4452 | A regression coefficient is the same thing as the slope of the line of the regression equation. The equation for the regression coefficient that you'll find on the AP Statistics test is: B1 = b1 = Σ [ (xi – x)(yi – y) ] / Σ [ (xi – x)2]. | |
C4453 | Decision Trees in Machine Learning. Decision Tree models are created using 2 steps: Induction and Pruning. Induction is where we actually build the tree i.e set all of the hierarchical decision boundaries based on our data. Because of the nature of training decision trees they can be prone to major overfitting. | |
C4454 | Steps to Making Your Frequency DistributionStep 1: Calculate the range of the data set. Step 2: Divide the range by the number of groups you want and then round up. Step 3: Use the class width to create your groups. Step 4: Find the frequency for each group. | |
C4455 | Normality of the residuals is an assumption of running a linear model. So, if your residuals are normal, it means that your assumption is valid and model inference (confidence intervals, model predictions) should also be valid. It's that simple! | |
C4456 | The Word2Vec Model This model was created by Google in 2013 and is a predictive deep learning based model to compute and generate high quality, distributed and continuous dense vector representations of words, which capture contextual and semantic similarity. | |
C4457 | XGboost is the most widely used algorithm in machine learning, whether the problem is a classification or a regression problem. It is known for its good performance as compared to all other machine learning algorithms. | |
C4458 | The difference is that PCA will try to reduce dimensionality by exploring how one feature of the data is expressed in terms of the other features(linear dependecy). Feature selection instead, takes the target into consideration. PCA is based on extracting the axes on which data shows the highest variability. | |
C4459 | Model calibration is done by adjusting the selected parameters such as growth rates, loss rates in the model to obtain a best fit between the model calculations and the monthly average field data (Set #1) collected during first year (June 18, 2004–June 27, 2005). | |
C4460 | Communalities – This is the proportion of each variable's variance that can be explained by the factors (e.g., the underlying latent continua). It is also noted as h2 and can be defined as the sum of squared factor loadings for the variables. They are the reproduced variances from the factors that you have extracted. | |
C4461 | The term convolution refers to the mathematical combination of two functions to produce a third function. It merges two sets of information. In the case of a CNN, the convolution is performed on the input data with the use of a filter or kernel (these terms are used interchangeably) to then produce a feature map. | |
C4462 | In statistics, the kth order statistic of a statistical sample is equal to its kth-smallest value. Together with rank statistics, order statistics are among the most fundamental tools in non-parametric statistics and inference. | |
C4463 | Agent, also called softbot (“software robot”), a computer program that performs various actions continuously and autonomously on behalf of an individual or an organization. For example, an agent may archive various computer files or retrieve electronic messages on a regular schedule. | |
C4464 | Sampling distributions are important for inferential statistics. In practice, one will collect sample data and, from these data, estimate parameters of the population distribution. Thus, knowledge of the sampling distribution can be very useful in making inferences about the overall population. | |
C4465 | frequency–inverse document frequency | |
C4466 | One way of finding a point estimate ˆx=g(y) is to find a function g(Y) that minimizes the mean squared error (MSE). Here, we show that g(y)=E[X|Y=y] has the lowest MSE among all possible estimators. That is why it is called the minimum mean squared error (MMSE) estimate. | |
C4467 | In mathematics, specifically in functional analysis, each bounded linear operator on a complex Hilbert space has a corresponding Hermitian adjoint (or adjoint operator). Adjoints of operators generalize conjugate transposes of square matrices to (possibly) infinite-dimensional situations. | |
C4468 | The parameters of LDA model have the prior distribution, and are estimated by Bayesian method. LDA model has attracted many scholars' attention since its start, but its mathematical theory is too complex to understand quickly. | |
C4469 | “Malicious use of AI,” they wrote in their 100-page report, “could threaten digital security (e.g. through criminals training machines to hack or socially engineer victims at human or superhuman levels of performance), physical security (e.g. non-state actors weaponizing consumer drones), and political security (e.g. | |
C4470 | Today, neural networks are used for solving many business problems such as sales forecasting, customer research, data validation, and risk management. For example, at Statsbot we apply neural networks for time-series predictions, anomaly detection in data, and natural language understanding. | |
C4471 | In the literature, the distinction between frames and semantic networks is actually rather blurred. However, the more structure a system has, the more likely it is to be termed a frame system rather than a semantic network. | |
C4472 | Area under the ROC Curve | |
C4473 | When a study's aim is to investigate a correlational relationship, however, we recommend sampling between 500 and 1,000 people. More participants in a study will always be better, but these numbers are a useful rule of thumb for researchers seeking to find out how many participants they need to sample. | |
C4474 | – Rejection sampling: reject samples disagreeing with evidence. – Markov chain Monte Carlo (MCMC): sample from a stochastic process. whose stationary distribution is the true posterior. | |
C4475 | The scope of regression toward the means is different from gambler's fallacy. Gambler's fallacy is predicting what is the result of the next event, but regression toward the means is talking about the trend of future events. Let's go back to the coin example, having a tail in the next toss is a specific event. | |
C4476 | bucketized_column. Represents discretized dense input bucketed by boundaries . | |
C4477 | 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. | |
C4478 | When analysing data, such as the grades earned by 100 students, it is possible to use both descriptive and inferential statistics in your analysis. Typically, in most research conducted on groups of people, you will use both descriptive and inferential statistics to analyse your results and draw conclusions. | |
C4479 | Agglomerative clustering uses a bottom-up approach, wherein each data point starts in its own cluster. These clusters are then joined greedily, by taking the two most similar clusters together and merging them. For each cluster, you further divide it down to two clusters until you hit the desired number of clusters. | |
C4480 | "A discrete variable is one that can take on finitely many, or countably infinitely many values", whereas a continuous random variable is one that is not discrete, i.e. "can take on uncountably infinitely many values", such as a spectrum of real numbers. | |
C4481 | 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. | |
C4482 | The difference between nonprobability and probability sampling is that nonprobability sampling does not involve random selection and probability sampling does. At least with a probabilistic sample, we know the odds or probability that we have represented the population well. | |
C4483 | The main aim of a sample size calculation is to determine the number of participants needed to detect a clinically relevant treatment effect. Pre-study calculation of the required sample size is warranted in the majority of quantitative studies. | |
C4484 | Tokenization is the process Stripe uses to collect sensitive card or bank account details, or personally identifiable information (PII), directly from your customers in a secure manner. A token representing this information is returned to your server to use. Tokens cannot be stored or used more than once. | |
C4485 | Class boundaries are values halfway between the upper class limit of one class and the lower class limit of the next. Class limits specify the span of data values that fall within a class. | |
C4486 | Regression is the statistical model that you use to predict a continuous outcome on the basis of one or more continuous predictor variables. In contrast, ANOVA is the statistical model that you use to predict a continuous outcome on the basis of one or more categorical predictor variables. | |
C4487 | Normal distributions are symmetric, unimodal, and asymptotic, and the mean, median, and mode are all equal. A normal distribution is perfectly symmetrical around its center. That is, the right side of the center is a mirror image of the left side. There is also only one mode, or peak, in a normal distribution. | |
C4488 | Statistical Methods for Finding the Best Regression ModelAdjusted R-squared and Predicted R-squared: Generally, you choose the models that have higher adjusted and predicted R-squared values. P-values for the predictors: In regression, low p-values indicate terms that are statistically significant.More items• | |
C4489 | Sampling is a statistical procedure that is concerned with the selection of the individual observation; it helps us to make statistical inferences about the population. In sampling, we assume that samples are drawn from the population and sample means and population means are equal. | |
C4490 | T = (X – μ) / [ σ/√(n) ]. This makes the equation identical to the one for the z-score; the only difference is you're looking up the result in the T table, not the Z-table. For sample sizes over 30, you'll get the same result. | |
C4491 | Transfer learning is useful when you have insufficient data for a new domain you want handled by a neural network and there is a big pre-existing data pool that can be transferred to your problem. | |
C4492 | According to Daniel Little, University of Michigan-Dearborn, an endogenous variable is defined in the following way: A variable xj is said to be endogenous within the causal model M if its value is determined or influenced by one or more of the independent variables X (excluding itself). | |
C4493 | In non-probability sampling, the sample is selected based on non-random criteria, and not every member of the population has a chance of being included. Common non-probability sampling methods include convenience sampling, voluntary response sampling, purposive sampling, snowball sampling, and quota sampling. | |
C4494 | So you are model-free. This is when you apply Q learning. With value iteration, you learn the expected cost when you are given a state x. With q-learning, you get the expected discounted cost when you are in state x and apply action a. | |
C4495 | The Word error rate (WER) is a metric based on the Levenshtein distance, where the Levenshtein distance works at the character level, WER works at the word level. It was originally used for measuring the performance of speech recognition systems, but is also used in the evaluation of machine translation. | |
C4496 | The XOr, or “exclusive or”, problem is a classic problem in ANN research. It is the problem of using a neural network to predict the outputs of XOr logic gates given two binary inputs. An XOr function should return a true value if the two inputs are not equal and a false value if they are equal. | |
C4497 | Hadoop is an open source, Java based framework used for storing and processing big data. The data is stored on inexpensive commodity servers that run as clusters. Cafarella, Hadoop uses the MapReduce programming model for faster storage and retrieval of data from its nodes. | |
C4498 | Time series data means that data is in a series of particular time periods or intervals. The data is considered in three types: Time series data: A set of observations on the values that a variable takes at different times. Cross-sectional data: Data of one or more variables, collected at the same point in time. | |
C4499 | In its simplest form, binary search is used to quickly find a value in a sorted sequence (consider a sequence an ordinary array for now). We'll call the sought value the target value for clarity. Binary search maintains a contiguous subsequence of the starting sequence where the target value is surely located. |
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