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C3400 | When the sample size is small, we use the t-distribution to calculate the p-value. In this case, we calculate the degrees of freedom, df= n-1. We then use df, along with the test statistic, to calculate the p-value. If the sample is greater than 30 (n>30), we consider this a large sample size. | |
C3401 | Significance level and p-value α is the maximum probability of rejecting the null hypothesis when the null hypothesis is true. If α = 1 we always reject the null, if α = 0 we never reject the null hypothesis. If we choose to compare the p-value to α = 0.01, we are insisting on a stronger evidence! | |
C3402 | Binning, bagging, and stacking, are basic parts of a data scientist's toolkit and a part of a series of statistical techniques called ensemble methods. Bagging to decrease the model's variance; Boosting to decreasing the model's bias, and; Stacking to increasing the predictive force of the classifier. | |
C3403 | The simplest solution is to use other activation functions, such as ReLU, which doesn't cause a small derivative. Residual networks are another solution, as they provide residual connections straight to earlier layers. | |
C3404 | Multi-task learning (MTL) is a subfield of machine learning in which multiple learning tasks are solved at the same time, while exploiting commonalities and differences across tasks. In the classification context, MTL aims to improve the performance of multiple classification tasks by learning them jointly. | |
C3405 | One of the ways to help deal with this bias is to avoid shaping participants' ideas or experiences before they are faced with the experimental material. Even stating seemingly innocuous details might prime an individual to form theories or thoughts that could bias their answers or behavior. | |
C3406 | Linear regression is a classical model for predicting a numerical quantity. Coefficients of a linear regression model can be estimated using a negative log-likelihood function from maximum likelihood estimation. The negative log-likelihood function can be used to derive the least squares solution to linear regression. | |
C3407 | It all depends on your end goal, if you want to experience the power of modern computer then go for Deep learning, but in DL you need some basic machine learning concepts. If you want to know how machines predict the weather or make their own artificial intelligence, then learn ML. | |
C3408 | regression of y on x - the equation representing the relation between selected values of one variable (x) and observed values of the other (y); it permits the prediction of the most probable values of y. regression equation. | |
C3409 | Basically, it takes between 365 days (1 year) to 1,825 days (5 years) to learn artificial intelligence (assuming you put in 4 – 0.5 learning hours a day). And how fast you learn also affects how long it takes you to be an expert. | |
C3410 | 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. | |
C3411 | Hyperparameters are the variables which determines the network structure(Eg: Number of Hidden Units) and the variables which determine how the network is trained(Eg: Learning Rate). Hyperparameters are set before training(before optimizing the weights and bias). | |
C3412 | A Markov chain is a regular Markov chain if its transition matrix is regular. For example, if you take successive powers of the matrix D, the entries of D will always be positive (or so it appears). So D would be regular. | |
C3413 | Anomaly detection, also known as outlier detection is the process of identifying extreme points or observations that are significantly deviating from the remaining data. Whereas in unsupervised learning, no labels are presented for data to train upon. | |
C3414 | Select a File for Image ChangeFrom the Toolbox, select Change Detection > Image Change Workflow. Select an input file from the File Selection dialog. To apply a mask, select the Input Mask tab in the File Selection panel. Select the Input Files tab again.Enter the path and filename for the Time 2 File. Click Next. | |
C3415 | k-means clustering is a method of vector quantization, originally from signal processing, that aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean (cluster centers or cluster centroid), serving as a prototype of the cluster. | |
C3416 | To find the shortest path, all you have to do is start from the source and perform a breadth first search and stop when you find your destination Node. The only additional thing you need to do is have an array previous[n] which will store the previous node for every node visited. The previous of source can be null. | |
C3417 | Take a labelled dataset, cluster it with the algorithm and interpret the results so expectation is to have same label instances in the same clusters. Use some kind of precision-recall, purity or entropy metrics for empirical results.Cluster data and compare with the randomly clustered data. | |
C3418 | Generalization refers to your model's ability to adapt properly to new, previously unseen data, drawn from the same distribution as the one used to create the model. Estimated Time: 5 minutes Learning Objectives. | |
C3419 | Some regression models are already classification models - e.g. logistic regression. Regression trees turn into classification trees if the dependent variable changes. Similarly, if you cateogorize the dependent variable, a linear regression is inappopriate and a logistic regression model is better. | |
C3420 | Focus on these key areas to lay the groundwork for successful AI implementations in your organizationExplore business opportunities.Assess your data needs.Examine your infrastructure.Determine your talent or vendor needs.Be prepared for inevitable risk. | |
C3421 | Blocking refers to classifying experimental units into blocks whereas stratification refers to classifying individuals of a population into strata. The samples from the strata in a stratified random sample can be the blocks in an experiment. | |
C3422 | 2 Answers. measures of central tendency are mean, mode and median , whereas measures of dispersion are variance, standard deviation and interquartile range (it explains the extent to which distribution stretched or squeezed). | |
C3423 | The dimensional equations have got the following uses: To check the correctness of a physical relation. To derive the relation between various physical quantities. To convert value of physical quantity from one system of unit to another system. | |
C3424 | You now know that: Bias is the simplifying assumptions made by the model to make the target function easier to approximate. Variance is the amount that the estimate of the target function will change given different training data. Trade-off is tension between the error introduced by the bias and the variance. | |
C3425 | The formula is:P(A|B) = P(A) P(B|A)P(B)P(Man|Pink) = P(Man) P(Pink|Man)P(Pink)P(Man|Pink) = 0.4 × 0.1250.25 = 0.2.Both ways get the same result of ss+t+u+v.P(A|B) = P(A) P(B|A)P(B)P(Allergy|Yes) = P(Allergy) P(Yes|Allergy)P(Yes)P(Allergy|Yes) = 1% × 80%10.7% = 7.48%More items | |
C3426 | Prevalence thus impacts the positive predictive value (PPV) and negative predictive value (NPV) of tests. As the prevalence increases, the PPV also increases but the NPV decreases. Similarly, as the prevalence decreases the PPV decreases while the NPV increases. | |
C3427 | Artificial intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. The term may also be applied to any machine that exhibits traits associated with a human mind such as learning and problem-solving. | |
C3428 | Overfitting is a modeling error that occurs when a function is too closely fit to a limited set of data points. Thus, attempting to make the model conform too closely to slightly inaccurate data can infect the model with substantial errors and reduce its predictive power. | |
C3429 | In the binomial distribution, the number of trials is fixed, and we count the number of "successes". Whereas, in the geometric and negative binomial distributions, the number of "successes" is fixed, and we count the number of trials needed to obtain the desired number of "successes". | |
C3430 | Len Gould. Answered November 6, 2016 · Author has 6.4K answers and 3M answer views. Outgroups are simply the people who are not members of your ingroup. Obvious examples of bases for forming ingroups are according to their race, culture, gender, age or religion. | |
C3431 | The t-value is specific thing for a specific statistical test, that means little by itself. The p-value tells you the statistical significance of the difference; the t-value is an intermediate step. This is the p-value. If p < alpha = 0.05, you have a statistically significant difference. | |
C3432 | Generative adversarial networks (GANs) are an exciting recent innovation in machine learning. GANs are generative models: they create new data instances that resemble your training data. For example, GANs can create images that look like photographs of human faces, even though the faces don't belong to any real person. | |
C3433 | Stationary Time Series Time series are stationary if they do not have trend or seasonal effects. Summary statistics calculated on the time series are consistent over time, like the mean or the variance of the observations. | |
C3434 | Definition A.I (fuzzy set) A fuzzy set A on universe (domain) X is defined by the membership function ILA{X) which is a mapping from the universe X into the unit interval: F{X) denotes the set of all fuzzy sets on X. Fuzzy set theory allows for a partial membership of an element in a set. | |
C3435 | Skewed data show a lopsided boxplot, where the median cuts the box into two unequal pieces. If the longer part of the box is to the right (or above) the median, the data is said to be skewed right. If one side of the box is longer than the other, it does not mean that side contains more data. | |
C3436 | The distributional hypothesis in linguistics is derived from the semantic theory of language usage, i.e. words that are used and occur in the same contexts tend to purport similar meanings. The underlying idea that "a word is characterized by the company it keeps" was popularized by Firth in the 1950s. | |
C3437 | Decision trees use multiple algorithms to decide to split a node into two or more sub-nodes. The creation of sub-nodes increases the homogeneity of resultant sub-nodes. The decision tree splits the nodes on all available variables and then selects the split which results in most homogeneous sub-nodes. | |
C3438 | To calculate the similarity between two examples, you need to combine all the feature data for those two examples into a single numeric value. For instance, consider a shoe data set with only one feature: shoe size. You can quantify how similar two shoes are by calculating the difference between their sizes. | |
C3439 | The shape of any distribution can be described by its various 'moments'. The first four are: 1) The mean, which indicates the central tendency of a distribution. 2) The second moment is the variance, which indicates the width or deviation. | |
C3440 | Data Streaming Explained Also known as event stream processing, streaming data is the continuous flow of data generated by various sources. By using stream processing technology, data streams can be processed, stored, analyzed, and acted upon as it's generated in real-time. | |
C3441 | Applications of association rule mining are stock analysis, web log mining, medical diagnosis, customer market analysis bioinformatics etc. In past, many algorithms were developed by researchers for Boolean and Fuzzy association rule mining such as Apriori, FP-tree, Fuzzy FP-tree etc. | |
C3442 | VGGAcronymDefinitionVGGVisual Geometry Group (UK)VGGVancouver Gaming Guild (Canada)VGGVery Great GameVGGVeterans Gaming Group3 more rows | |
C3443 | Factor analysis is a multivariant mathematical technique traditionally used in psychometrics to construct measures of psychologic and behavioral characteristics, such as intellectual abilities or personality traits (12). | |
C3444 | For a discrete random variable, the expected value, usually denoted as or , is calculated using: μ = E ( X ) = ∑ x i f ( x i ) | |
C3445 | Definition 1 (Minimal Sufficiency). A sufficient statistic T is minimal if for every sufficient statistic T and for every x, y ∈ X, T(x) = T(y) whenever T (x) = T (y). In other words, T is a function of T (there exists f such that T(x) = f(T (x)) for any x ∈ X). | |
C3446 | Concepts in Feature Space Given a set of features for a concept learning problem, we can interpret the feature set as a feature space. Given some data, a feature space is just the set of all possible values for a chosen set of features from that data. | |
C3447 | Chi-squared test for nominal (categorical) data. The c2 test is used to determine whether an association (or relationship) between 2 categorical variables in a sample is likely to reflect a real association between these 2 variables in the population. | |
C3448 | Random Forest | |
C3449 | A simple random sample is used to represent the entire data population and. randomly selects individuals from the population without any other consideration. A stratified random sample, on the other hand, first divides the population into smaller groups, or strata, based on shared characteristics. | |
C3450 | A kNN algorithm is an extreme form of instance-based methods because all training observations are retained as a part of the model. It is a competitive learning algorithm because it internally uses competition between model elements (data instances) to make a predictive decision. | |
C3451 | The pca. explained_variance_ratio_ parameter returns a vector of the variance explained by each dimension. That will return a vector x such that x[i] returns the cumulative variance explained by the first i+1 dimensions. | |
C3452 | Non parametric do not assume that the data is normally distributed. For example: the Kruskal Willis test is the non parametric alternative to the One way ANOVA and the Mann Whitney is the non parametric alternative to the two sample t test. The main nonparametric tests are: 1-sample sign test. | |
C3453 | Relative Risk is calculated by dividing the probability of an event occurring for group 1 (A) divided by the probability of an event occurring for group 2 (B). Relative Risk is very similar to Odds Ratio, however, RR is calculated by using percentages, whereas Odds Ratio is calculated by using the ratio of odds. | |
C3454 | A rule-based system (e.g., production system, expert system) uses rules as the knowledge representation. These rules are coded into the system in the form of if-then-else statements. So, let's regard rule-based systems as the simplest form of AI. | |
C3455 | These models, when used as inputs of ensemble methods, are called ”base models”. In this blog post I will cover ensemble methods for classification and describe some widely known methods of ensemble: voting, stacking, bagging and boosting. | |
C3456 | Gradient descent is an optimization algorithm used to minimize some function by iteratively moving in the direction of steepest descent as defined by the negative of the gradient. In machine learning, we use gradient descent to update the parameters of our model. | |
C3457 | The expected value of the sample mean is equal to the population mean µ. Therefore, the sample mean is an unbiased estimator of the population mean. Since only a sample of observations is available, the estimate of the mean can be either less than or greater than the true population mean. | |
C3458 | Answer. A negative path loading is basically the same as a negative regression coefficient. I.e., For a path loading from X to Y it is the predicted increase in Y for a one unit increase on X holding all other variables constant. So a negative coefficient just means that as X increases, Y is predicted to decrease. | |
C3459 | Simply put, an activation function is a function that is added into an artificial neural network in order to help the network learn complex patterns in the data. When comparing with a neuron-based model that is in our brains, the activation function is at the end deciding what is to be fired to the next neuron. | |
C3460 | P > 0.05 is the probability that the null hypothesis is true. 1 minus the P value is the probability that the alternative hypothesis is true. A statistically significant test result (P ≤ 0.05) means that the test hypothesis is false or should be rejected. A P value greater than 0.05 means that no effect was observed. | |
C3461 | One of the most common structures that text mining packages work with is the document-term matrix (or DTM). This is a matrix where: each row represents one document (such as a book or article), each column represents one term, and. each value (typically) contains the number of appearances of that term in that document. | |
C3462 | This article lists out 10 comprehensive data mining tools widely used in the big data industry.Rapid Miner. Oracle Data Mining. IBM SPSS Modeler. KNIME. Python. Orange. Kaggle. Rattle.More items• | |
C3463 | The aim of distributional semantics is to learn the meanings of linguistic expressions from a corpus of text. The core idea, known as the distributional hy- pothesis, is that the contexts in which an expression appears give us information about its meaning. | |
C3464 | “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. | |
C3465 | Stratified random sampling involves first dividing a population into subpopulations and then applying random sampling methods to each subpopulation to form a test group. A disadvantage is when researchers can't classify every member of the population into a subgroup. | |
C3466 | A pooling layer is a new layer added after the convolutional layer. Specifically, after a nonlinearity (e.g. ReLU) has been applied to the feature maps output by a convolutional layer; for example the layers in a model may look as follows: Input Image. Convolutional Layer. | |
C3467 | - Mode-The most repetitive number! - Median:The number in the MIDDLE when they are IN ORDER! - Mean- The AVERAGE OF ALL NUMBERS: You add up all the numbers then you divide it by the TOTAL NUMBER of NUMBERS! - Range - THE BIGGEST minus the Smallest! | |
C3468 | The experts predict that AI will outperform humans in the next 10 years in tasks such as translating languages (by 2024), writing high school essays (by 2026), and driving trucks (by 2027). But many other tasks will take much longer for machines to master. | |
C3469 | A classification problem is when the output variable is a category, such as “red” or “blue” or “disease” and “no disease”. A classification model attempts to draw some conclusion from observed values. Given one or more inputs a classification model will try to predict the value of one or more outcomes. | |
C3470 | Neural networks are designed to work just like the human brain does. In the case of recognizing handwriting or facial recognition, the brain very quickly makes some decisions. For example, in the case of facial recognition, the brain might start with “It is female or male? | |
C3471 | The fundamental reason to use a random forest instead of a decision tree is to combine the predictions of many decision trees into a single model. The logic is that a single even made up of many mediocre models will still be better than one good model. | |
C3472 | one training example | |
C3473 | Bayesian model averaging (BMA) is an extension of the usual Bayesian inference methods in which one does not only describe parameter uncertainty through the prior distribution but also model uncertainty obtaining posterior distributions for model parameters and for the model themselves using Bayes' theorem, allowing | |
C3474 | The first order statistic is the smallest sample value (i.e. the minimum), once the values have been placed in order. For example, in the sample 9, 2, 11, 5, 7, 4 the first order statistic is 2. In notation, that's x(1) = 2. The second order statistic x(2) is the next smallest value. | |
C3475 | Text classification using word embeddings and deep learning in python — classifying tweets from twitterSplit the data into text (X) and labels (Y)Preprocess X.Create a word embedding matrix from X.Create a tensor input from X.Train a deep learning model using the tensor inputs and labels (Y)More items• | |
C3476 | Definition. A study design that randomly assigns participants into an experimental group or a control group. As the study is conducted, the only expected difference between the control and experimental groups in a randomized controlled trial (RCT) is the outcome variable being studied. | |
C3477 | Bayesian inference refers to statistical inference where uncertainty in inferences is quantified using probability. Statistical models specify a set of statistical assumptions and processes that represent how the sample data is generated. Statistical models have a number of parameters that can be modified. | |
C3478 | A generative model on the other hand will be able to produce a new picture of a either class. Typical discriminative models include logistic regression (LR), support vector machines (SVM), conditional random fields (CRFs) (specified over an undirected graph), decision trees, neural networks, and many others. | |
C3479 | The dimension of a data set is the number of columns. The rows are the number of samples, usually. | |
C3480 | Splitting: Dividing a node into two or more sub-nodes based on if-else conditions. Decision Node: After splitting the sub-nodes into further sub-nodes, then it is called as the decision node. Leaf or Terminal Node: This is the end of the decision tree where it cannot be split into further sub-nodes. | |
C3481 | Connectionism is a movement in cognitive science that hopes to explain intellectual abilities using artificial neural networks (also known as “neural networks” or “neural nets”). These weights model the effects of the synapses that link one neuron to another. | |
C3482 | Bivariate statistics is a type of inferential statistics that deals with the relationship between two variables. When bivariate statistics is employed to examine a relationship between two variables, bivariate data is used. Bivariate data consists of data collected from a sample on two different variables. | |
C3483 | Any sum or difference or independent normal random variables is also normally distributed. A binomial setting arises when we perform several independent trials of the same chance process and record the number of times a particular outcome occurs. | |
C3484 | Parametric tests are used only where a normal distribution is assumed. The most widely used tests are the t-test (paired or unpaired), ANOVA (one-way non-repeated, repeated; two-way, three-way), linear regression and Pearson rank correlation. | |
C3485 | A residual neural network (ResNet) is an artificial neural network (ANN) of a kind that builds on constructs known from pyramidal cells in the cerebral cortex. Residual neural networks do this by utilizing skip connections, or shortcuts to jump over some layers. | |
C3486 | Linear regression is the analysis of two separate variables to define a single relationship and is a useful measure for technical and quantitative analysis in financial markets. Using linear regression, a trader can identify key price points—entry price, stop-loss price, and exit prices. | |
C3487 | In a courtroom, a Type 2 error is acquitting a guilty person. A Type 1 error is when you incorrectly reject the null when it is true. If the p -value is small, then you have observed something rare if the null is true. This then provides evidence against the truth of H0 . | |
C3488 | 2 Answers. The multinomial distribution is when there are multiple identical independent trials where each trial has k possible outcomes. The categorical distribution is when there is only one such trial. | |
C3489 | Types of selection bias The most common type of selection bias in research or statistical analysis is a sample selection bias. In principle, the bias can occur through selection effects in other aspects of the research process, such as which variables to use in analysis, and which tools to use to perform measurement. | |
C3490 | Vectors can be used to represent physical quantities. Most commonly in physics, vectors are used to represent displacement, velocity, and acceleration. Vectors are a combination of magnitude and direction, and are drawn as arrows. | |
C3491 | In mathematics, a nonnegative matrix, written. is a matrix in which all the elements are equal to or greater than zero, that is, A positive matrix is a matrix in which all the elements are strictly greater than zero. | |
C3492 | Electroencephalogram (EEG) spectral analysis quantifies the amount of rhythmic (or oscillatory) activity of different frequency in EEGs. Despite the tremendous amount of research related to its usefulness, EEG spectral analysis still exhibits inconsistent results among studies. | |
C3493 | The Poisson(λ) Distribution can be approximated with Normal when λ is large. For sufficiently large values of λ, (say λ>1,000), the Normal(μ = λ,σ2 = λ) Distribution is an excellent approximation to the Poisson(λ) Distribution. | |
C3494 | Many algorithms have been used in measuring user similarity or item similarity in recommender systems. For example, the k-nearest neighbor (k-NN) approach and the Pearson Correlation as first implemented by Allen. | |
C3495 | Data Preprocessing is a technique that is used to convert the raw data into a clean data set. In other words, whenever the data is gathered from different sources it is collected in raw format which is not feasible for the analysis. | |
C3496 | If you establish at least a moderate correlation between X and Y through both a correlation coefficient and a scatterplot, then you know they have some type of linear relationship. Never do a regression analysis unless you have already found at least a moderately strong correlation between the two variables. | |
C3497 | Textual entailment (TE) in natural language processing is a directional relation between text fragments. The relation holds whenever the truth of one text fragment follows from another text. In the TE framework, the entailing and entailed texts are termed text (t) and hypothesis (h), respectively. | |
C3498 | In the real world, knowledge plays a vital role in intelligence as well as creating artificial intelligence. It demonstrates the intelligent behavior in AI agents or systems. It is possible for an agent or system to act accurately on some input only when it has the knowledge or experience about the input. | |
C3499 | Variational approximations is a body of deterministic tech- niques for making approximate inference for parameters in complex statistical models. |
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