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C2400 | 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. | |
C2401 | The probability density function (PDF) is defined for probability distributions of continuous random variables. The probability at a certain point of a continuous variable is zero. The cumulative distribution function (CDF) is a non-decreasing function as the probabilities can never be less than 0. | |
C2402 | The resulting learned residual allows our network to theoretically do no worse (than without it). Peephole connections redirect the cell state as input to the LSTM input, output, and forget gates. These connections are used to learn precise timings. | |
C2403 | The frequency of a class interval is the number of data values that fall in the range specified by the interval. The size of the class interval is often selected as 5, 10, 15 or 20 etc. Each class interval starts at a value that is a multiple of the size. | |
C2404 | Data = true signal + noise Noisy data are data with a large amount of additional meaningless information in it called noise. This includes data corruption and the term is often used as a synonym for corrupt data. It also includes any data that a user system cannot understand and interpret correctly. | |
C2405 | Sensitivity refers to a test's ability to designate an individual with disease as positive. A highly sensitive test means that there are few false negative results, and thus fewer cases of disease are missed. The specificity of a test is its ability to designate an individual who does not have a disease as negative. | |
C2406 | In-group favoritism, sometimes known as in-group–out-group bias, in-group bias, intergroup bias, or in-group preference, is a pattern of favoring members of one's in-group over out-group members. This can be expressed in evaluation of others, in allocation of resources, and in many other ways. | |
C2407 | Control Charts: A discrete distribution is one in which the data can only take on certain values, for example integers. A continuous distribution is one in which data can take on any value within a specified range (which may be infinite). | |
C2408 | The definition of an endogenous variable, exogenous variable and parameter are as follows: An Endogenous Variable- is a variable whose value is determined within the model itself. An Exogenous Variable – is a variable whose value is assumed to be determined outside the model. | |
C2409 | The Hidden Markov Model (HMM) is a relatively simple way to model sequential data. A hidden Markov model implies that the Markov Model underlying the data is hidden or unknown to you. More specifically, you only know observational data and not information about the states. | |
C2410 | Every probability pi is a number between 0 and 1, and the sum of all the probabilities is equal to 1. Examples of discrete random variables include: The number of eggs that a hen lays in a given day (it can't be 2.3) The number of people going to a given soccer match. | |
C2411 | occurs when individuals or groups in a study differ systematically from the population of interest leading to a systematic error in an association or outcome. | |
C2412 | This makes it easy for you to quickly see which variable is independent and which is dependent when looking at a graph or chart. The independent variable always goes on the x-axis, or the horizontal axis. The dependent variable goes on the y-axis, or vertical axis. | |
C2413 | The first benefit of time series analysis is that it can help to clean data. This makes it possible to find the true “signal” in a data set, by filtering out the noise. This can mean removing outliers, or applying various averages so as to gain an overall perspective of the meaning of the data. | |
C2414 | So the difference is in the way the future reward is found. In Q-learning it's simply the highest possible action that can be taken from state 2, and in SARSA it's the value of the actual action that was taken. | |
C2415 | In the domain of physics and probability, a Markov random field (often abbreviated as MRF), Markov network or undirected graphical model is a set of random variables having a Markov property described by an undirected graph. The underlying graph of a Markov random field may be finite or infinite. | |
C2416 | Change in either of Proximity function, no. of data points or no. of variables will lead to different clustering results and hence different dendrograms. | |
C2417 | How to Protect Against Confirmation Bias Find someone who disagrees with a decision you're about to make. Ask them why they disagree with you. Carefully listen to what they have to say. Continue listening until you can honestly say, “I now understand why you believe that.” | |
C2418 | Bootstrapping is a technique used by individuals in business to overcome obstacles, achieve goals and make improvements through organic, self-sustainable means with no assistance from outside. | |
C2419 | Relative frequencies can be written as fractions, percents, or decimals. The column should add up to 1 (or 100%). The only difference between a relative frequency distribution graph and a frequency distribution graph is that the vertical axis uses proportional or relative frequency rather than simple frequency. | |
C2420 | Difference between multi-class classification & multi-label classification is that in multi-class problems the classes are mutually exclusive, whereas for multi-label problems each label represents a different classification task, but the tasks are somehow related. | |
C2421 | The CVT is an automatic transmission that uses two pulleys with a steel belt running between them. To continuously vary its gear ratios, the CVT simultaneously adjusts the diameter of the "drive pulley" that transmits torque from the engine and the "driven pulley" that transfers torque to the wheels. | |
C2422 | Machine Learning is a set of algorithms that parse data and learns from the parsed data and use those learnings to discover patterns of interest. Neural Network or Artificial Neural Network is one set of algorithms used in machine learning for modeling the data using graphs of Neurons. | |
C2423 | jackknifing is calculation with data sets sampled randomly from the original data. Bootstrapping is similar to jackknifing except that the position chosen at random may include multiple copies of the same position, to form data sets of the same size as original, to preserve statistical properties of data sampling. | |
C2424 | In natural language processing, the latent Dirichlet allocation (LDA) is a generative statistical model that allows sets of observations to be explained by unobserved groups that explain why some parts of the data are similar. | |
C2425 | In Convolutional neural network, the kernel is nothing but a filter that is used to extract the features from the images. The kernel is a matrix that moves over the input data, performs the dot product with the sub-region of input data, and gets the output as the matrix of dot products. | |
C2426 | 1. Why is the XOR problem exceptionally interesting to neural network researchers? Explanation: Linearly separable problems of interest of neural network researchers because they are the only class of problem that Perceptron can solve successfully. | |
C2427 | The Paired Samples t Test compares two means that are from the same individual, object, or related units. The two means can represent things like: A measurement taken at two different times (e.g., pre-test and post-test with an intervention administered between the two time points)5 päivää sitten | |
C2428 | The binomial distribution model allows us to compute the probability of observing a specified number of "successes" when the process is repeated a specific number of times (e.g., in a set of patients) and the outcome for a given patient is either a success or a failure. The binomial equation also uses factorials. | |
C2429 | Simple Random Sample vs. Random Sample A simple random sample is similar to a random sample. The difference between the two is that with a simple random sample, each object in the population has an equal chance of being chosen. With random sampling, each object does not necessarily have an equal chance of being chosen. | |
C2430 | The Range is the difference between the lowest and highest values. Example: In {4, 6, 9, 3, 7} the lowest value is 3, and the highest is 9. So the range is 9 − 3 = 6. | |
C2431 | A bandit is a robber, thief, or outlaw. A bandit typically belongs to a gang of bandits who commit crimes in remote, lawless, or out-of-the-way places. | |
C2432 | A discrete variable is a variable which can only take a countable number of values. In this example, the number of heads can only take 4 values (0, 1, 2, 3) and so the variable is discrete. The variable is said to be random if the sum of the probabilities is one. | |
C2433 | Note: a Markov chain (of any order) is a stochastic recursive sequence of finite order, or equivalently an auto-regressive process of finite order (possibly nonlinear). In contrast, the martingale property does not put constraints on the order of recursion, while imposing a linear projection condition. | |
C2434 | The standard deviation is the square root of the variance. Use a calculator to find the square root, and the result is the standard deviation. Report your result. Using this calculation, the precision of the scale can be represented by giving the mean, plus or minus the standard deviation. | |
C2435 | The length of time between each transit is the planet's "orbital period", or the length of a year on that particular planet. Not all planets have years as long as a year on the Earth! Some planets discovered by Kepler orbit around their stars so quickly that their years only last about four hours! | |
C2436 | Statistical learning plays a key role in many areas of science, finance and industry. Some more examples of the learning problems are: Predict whether a patient, hospitalized due to a heart attack, will have a second heart attack. | |
C2437 | The Boruta algorithm is a wrapper built around the random forest classification algorithm. It tries to capture all the important, interesting features you might have in your dataset with respect to an outcome variable. First, it duplicates the dataset, and shuffle the values in each column. | |
C2438 | Accuracy refers to the closeness of a measured value to a standard or known value. Precision refers to the closeness of two or more measurements to each other. Using the example above, if you weigh a given substance five times, and get 3.2 kg each time, then your measurement is very precise. | |
C2439 | The maximum or minimum over the entire function is called an "Absolute" or "Global" maximum or minimum. There is only one global maximum (and one global minimum) but there can be more than one local maximum or minimum. Assuming this function continues downwards to left or right: The Global Maximum is about 3.7. | |
C2440 | Multivariate data analysis is a set of statistical models that examine patterns in multidimensional data by considering, at once, several data variables. It is an expansion of bivariate data analysis, which considers only two variables in its models. | |
C2441 | Greedy Search This strategy selects the most probable word (i.e. argmax) from the model's vocabulary at each decoding time-step as the candidate to output sequence. | |
C2442 | Harmonic means are often used in averaging things like rates (e.g., the average travel speed given a duration of several trips). The weighted harmonic mean is used in finance to average multiples like the price-earnings ratio because it gives equal weight to each data point. | |
C2443 | If you are broadcasting or reinforcing sound outside, and even your best windscreen can't keep out the persistent low-frequency rumble from wind noise, then stopping it right at the source may be your best option. Highpass filters are excellent for this application. | |
C2444 | 1 Answer. Normalized discounted cumulative gain is one of the standard method of evaluating ranking algorithms. You will need to provide a score to each of the recommendations that you give. If your algorithm assigns a low (better) rank to a high scoring entity, your NDCG score will be higher, and vice versa. | |
C2445 | Bayesian hyperparameter tuning allows us to do so by building a probabilistic model for the objective function we are trying to minimize/maximize in order to train our machine learning model. Examples of such objective functions are not scary - accuracy, root mean squared error and so on. | |
C2446 | Just as correlation measures the extent of a linear relationship between two variables, autocorrelation measures the linear relationship between lagged values of a time series. There are several autocorrelation coefficients, corresponding to each panel in the lag plot. | |
C2447 | Typically, with neural networks, we seek to minimize the error. As such, the objective function is often referred to as a cost function or a loss function and the value calculated by the loss function is referred to as simply “loss.” | |
C2448 | SummaryLoad EMNIST digits from the Extra Keras Datasets module.Prepare the data.Define and train a Convolutional Neural Network for classification.Save the model.Load the model.Generate new predictions with the loaded model and validate that they are correct. | |
C2449 | TL; DR: The naive Bayes classifier is an approximation to the Bayes classifier, in which we assume that the features are conditionally independent given the class instead of modeling their full conditional distribution given the class. A Bayes classifier is best interpreted as a decision rule. | |
C2450 | Estimation is the process used to calculated these population parameters by analyzing only a small random sample from the population. The value or range of values used to approximate a parameter is called an estimate. | |
C2451 | An independent variable is defined within the context of a dependent variable. In the context of a model the independent variables are input whereas the dependent variables are the targets (Input vs Output). An exogenous variable is a variable whose state is independent of the state of other variables in a system. | |
C2452 | Lasso Regression Another Tolerant Method for dealing with multicollinearity known as Least Absolute Shrinkage and Selection Operator (LASSO) regression, solves the same constrained optimization problem as ridge regression, but uses the L1 norm rather than the L2 norm as a measure of complexity. | |
C2453 | In the study of probability theory, the central limit theorem (CLT) states that the distribution of sample approximates a normal distribution (also known as a “bell curve”) as the sample size becomes larger, assuming that all samples are identical in size, and regardless of the population distribution shape. | |
C2454 | Correlation is the process of moving a filter mask often referred to as kernel over the image and computing the sum of products at each location. Correlation is the function of displacement of the filter. | |
C2455 | Why You Should Care About the Classical OLS Assumptions In a nutshell, your linear model should produce residuals that have a mean of zero, have a constant variance, and are not correlated with themselves or other variables. | |
C2456 | In the machine learning world, offline learning refers to situations where the program is not operating and taking in new information in real time. Instead, it has a static set of input data. The opposite is online learning, where the machine learning program is working in real time on data that comes in. | |
C2457 | Outgroup homogeneity is the tendency for members of a group to see themselves as more diverse and heterogeneous than they are seen by an outgroup. Thus, for example, whereas Italians see themselves as quite diverse and different from one another, Americans view Italians as more similar to each other, or more alike. | |
C2458 | 6 Freebies to Help You Increase the Performance of Your Object Detection ModelsVisually Coherent Image Mix-up for Object Detection (+3.55% mAP Boost)Classification Head Label Smoothening (+2.16% mAP Boost)Data Pre-processing (Mixed Results)Training Scheduler Revamping (+1.44% mAP Boost)More items | |
C2459 | In statistics and probability analysis, the expected value is calculated by multiplying each of the possible outcomes by the likelihood each outcome will occur and then summing all of those values. By calculating expected values, investors can choose the scenario most likely to give the desired outcome. | |
C2460 | 4. A size of 100 means the vector representing each document will contain 100 elements - 100 values. The vector maps the document to a point in 100 dimensional space. A size of 200 would map a document to a point in 200 dimensional space. The more dimensions, the more differentiation between documents. | |
C2461 | One reason this is done is because the normal distribution often describes the actual distribution of the random errors in real-world processes reasonably well. Some methods, like maximum likelihood, use the distribution of the random errors directly to obtain parameter estimates. | |
C2462 | In statistics, a type of probability distribution in which all outcomes are equally likely. A coin also has a uniform distribution because the probability of getting either heads or tails in a coin toss is the same. | |
C2463 | Separating data into training and testing sets is an important part of evaluating data mining models. By using similar data for training and testing, you can minimize the effects of data discrepancies and better understand the characteristics of the model. | |
C2464 | Advantages and disadvantagesAre simple to understand and interpret. Have value even with little hard data. Help determine worst, best and expected values for different scenarios.Use a white box model. Can be combined with other decision techniques. | |
C2465 | Probability density function (PDF) is a statistical expression that defines a probability distribution (the likelihood of an outcome) for a discrete random variable (e.g., a stock or ETF) as opposed to a continuous random variable. | |
C2466 | Low-shot learning deep learning is based on the concept that reliable algorithms can be created to make predictions from minimalist datasets. | |
C2467 | Definition. Multivariate statistics refers to methods that examine the simultaneous effect of multiple variables. Traditional classification of multivariate statistical methods suggested by Kendall is based on the concept of dependency between variables (Kendall 1957). | |
C2468 | A local minimum of a function is a point where the function value is smaller than at nearby points, but possibly greater than at a distant point. A global minimum is a point where the function value is smaller than at all other feasible points. | |
C2469 | 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. | |
C2470 | In Average linkage clustering, the distance between two clusters is defined as the average of distances between all pairs of objects, where each pair is made up of one object from each group. D(r,s) = Trs / ( Nr * Ns) Where Trs is the sum of all pairwise distances between cluster r and cluster s. | |
C2471 | The only difference between proportionate and disproportionate stratified random sampling is their sampling fractions. If the researcher commits mistakes in allotting sampling fractions, a stratum may either be overrepresented or underrepresented which will result in skewed results. | |
C2472 | pooling layers are used to down sample the volume of convolution neural network by reducing the small translation of the features. pooling layer also provides a parameter reduction. | |
C2473 | Definition: A hash algorithm is a function that converts a data string into a numeric string output of fixed length. The output string is generally much smaller than the original data. Two of the most common hash algorithms are the MD5 (Message-Digest algorithm 5) and the SHA-1 (Secure Hash Algorithm). | |
C2474 | Maximum likelihood estimation refers to using a probability model for data and optimizing the joint likelihood function of the observed data over one or more parameters. Bayesian estimation is a bit more general because we're not necessarily maximizing the Bayesian analogue of the likelihood (the posterior density). | |
C2475 | The standard deviation (SD) measures the amount of variability, or dispersion, from the individual data values to the mean, while the standard error of the mean (SEM) measures how far the sample mean of the data is likely to be from the true population mean. The SEM is always smaller than the SD. | |
C2476 | Restricted Boltzmann machines (RBMs) have been used as generative models of many different types of data. RBMs are usually trained using the contrastive divergence learning procedure. This requires a certain amount of practical experience to decide how to set the values of numerical meta-parameters. | |
C2477 | It cannot be maintained that explanation and prediction are identical from the standpoint of their logical structure, the sole point of difference between them being one of content, in that the hypothesis of a prediction concerns the future, while explanations concern the past. | |
C2478 | Machine Learning(ML) generally means that you're training the machine to do something(here, image processing) by providing set of training data's. | |
C2479 | Cross-validation is a technique for evaluating ML models by training several ML models on subsets of the available input data and evaluating them on the complementary subset of the data. In k-fold cross-validation, you split the input data into k subsets of data (also known as folds). | |
C2480 | Traditionally in linear regression your predictors must either be continuous or binary. Ordinal variables are often inserted using a dummy coding scheme. This is equivalent to conducting an ANOVA and the baseline ordinal level will be represented by the intercept. | |
C2481 | Standardized effect size statistics remove the units of the variables in the effect. The second type is simple. These statistics describe the size of the effect, but remain in the original units of the variables. So for example, say you're comparing the mean temperature of soil under two different conditions. | |
C2482 | Motivation. Since the range of values of raw data varies widely, in some machine learning algorithms, objective functions will not work properly without normalization. Therefore, the range of all features should be normalized so that each feature contributes approximately proportionately to the final distance. | |
C2483 | While machine learning uses simpler concepts, deep learning works with artificial neural networks, which are designed to imitate how humans think and learn. It can be used to solve any pattern recognition problem and without human intervention. Artificial neural networks, comprising many layers, drive deep learning. | |
C2484 | t-test is used to test if two sample have the same mean. The assumptions are that they are samples from normal distribution. f-test is used to test if two sample have the same variance. Same assumptions hold. | |
C2485 | Part 6: Improve Deep Learning Models performance & network tuning.Increase model capacity.To increase the capacity, we add layers and nodes to a deep network (DN) gradually. The tuning process is more empirical than theoretical. Model & dataset design changes.Dataset collection & cleanup.Data augmentation.More items | |
C2486 | AI is used to augment human thinking and solve complex problems. It concentrates more on providing accurate results. Cognitive thinking, on the other hand, aims at mimicking human behavior and adapting to human reasoning, aiming to solve complex problems in a manner similar to the way humans would solve them. | |
C2487 | Deep-learning software by nameSoftwareCreatorInterfacePlaidMLVertex.AI, IntelPython, C++PyTorchAdam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan (Facebook)Python, C++, JuliaApache SINGAApache Software FoundationPython, C++, JavaTensorFlowGoogle BrainPython (Keras), C/C++, Java, Go, JavaScript, R, Julia, Swift18 riviä lisää | |
C2488 | There is no equivalent. A Kruskal Wallis is a non-parametric test. You say “is this difference larger than I would expect by chance”. You don't have a parameter, which is the size of the difference. | |
C2489 | There are two main types of image processing: image filtering and image warping. Two commonly implemented filters are the moving average filter and the image segmentation filter. The moving average filter replaces each pixel with the average pixel value of it and a neighborhood window of adjacent pixels. | |
C2490 | An autoregressive model is when a value from a time series is regressed on previous values from that same time series. The order of an autoregression is the number of immediately preceding values in the series that are used to predict the value at the present time. | |
C2491 | In a dataset a training set is implemented to build up a model, while a test (or validation) set is to validate the model built. Data points in the training set are excluded from the test (validation) set. | |
C2492 | Statistics is a mathematically-based field which seeks to collect and interpret quantitative data. In contrast, data science is a multidisciplinary field which uses scientific methods, processes, and systems to extract knowledge from data in a range of forms. | |
C2493 | The major factor affects standard error of the mean is sample size. The size of the sample increases the standard error of the mean decreases. Another factor affecting the standard error of the mean is the size of the population standard deviation. | |
C2494 | Simple linear regression is useful for finding relationship between two continuous variables. One is predictor or independent variable and other is response or dependent variable. It looks for statistical relationship but not deterministic relationship. | |
C2495 | A machine learning model is a file that has been trained to recognize certain types of patterns. You train a model over a set of data, providing it an algorithm that it can use to reason over and learn from those data. See Get ONNX models for Windows ML for more information. | |
C2496 | What are the five steps in the backpropagation learning algorithm?Initialize weights with random values and set other parameters.Read in the input vector and the desired output.Compute the actual output via the calculations, working forward through the layers. | |
C2497 | Each class will have a “lower class limit” and an “upper class limit” which are the lowest and highest numbers in each class. The “class width” is the distance between the lower limits of consecutive classes. The range is the difference between the maximum and minimum data entries. | |
C2498 | Techniques for performance improvement with model optimizationFine tuning the model with subset data >> Dropping few data samples for some of the overly sampled data classes.Class weights >> Used to train highly imbalanced (biased) database, class weights will give equal importance to all the classes during training.More items | |
C2499 | Checking if two categorical variables are independent can be done with Chi-Squared test of independence. This is a typical Chi-Square test: if we assume that two variables are independent, then the values of the contingency table for these variables should be distributed uniformly. |
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