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C3500 | Two main statistical methods are used in data analysis: descriptive statistics, which summarize data from a sample using indexes such as the mean or standard deviation, and inferential statistics, which draw conclusions from data that are subject to random variation (e.g., observational errors, sampling variation). | |
C3501 | A frequent problem in estimating logistic regression models is a failure of the likelihood maximization algorithm to converge. In most cases, this failure is a consequence of data patterns known as complete or quasi-complete separation. Log-likelihood as a function of the slope, quasi-complete separation. | |
C3502 | Logistic Regression not only gives a measure of how relevant a predictor (coefficient size) is, but also its direction of association (positive or negative). 4. Logistic regression is easier to implement, interpret and very efficient to train. | |
C3503 | Gradient descent techniques are known to be limited by a characteristic referred to as the `local minima' problem. During the search for an optimum solution or global minima, these techniques can encounter local minima from which they cannot escape due to the `steepest descent' nature of the approach. | |
C3504 | Bayesian Belief Network or Bayesian Network or Belief Network is a Probabilistic Graphical Model (PGM) that represents conditional dependencies between random variables through a Directed Acyclic Graph (DAG). | |
C3505 | So, How Does a Neural Network Work Exactly?Information is fed into the input layer which transfers it to the hidden layer.The interconnections between the two layers assign weights to each input randomly.A bias added to every input after weights are multiplied with them individually.More items• | |
C3506 | A false positive state is when the IDS identifies an activity as an attack but the activity is acceptable behavior. A false positive is a false alarm. This is when the IDS identifies an activity as acceptable when the activity is actually an attack. That is, a false negative is when the IDS fails to catch an attack. | |
C3507 | An OUTCOME (or SAMPLE POINT) is the result of a the experiment. The set of all possible outcomes or sample points of an experiment is called the SAMPLE SPACE. An EVENT is a subset of the sample space. | |
C3508 | average | |
C3509 | Timestep = the len of the input sequence. For example, if you want to give LSTM a sentence as an input, your timesteps could either be the number of words or the number of characters depending on what you want. Number of hidden units = (well) number of hidden units. Sometimes, people call this number of LSTM cells. | |
C3510 | In most basic probability theory courses your told moment generating functions (m.g.f) are useful for calculating the moments of a random variable. In particular the expectation and variance. Now in most courses the examples they provide for expectation and variance can be solved analytically using the definitions. | |
C3511 | Multidimensional scaling is a visual representation of distances or dissimilarities between sets of objects. Objects that are more similar (or have shorter distances) are closer together on the graph than objects that are less similar (or have longer distances). | |
C3512 | Suppose it is of interest to estimate the population mean, μ, for a quantitative variable. Data collected from a simple random sample can be used to compute the sample mean, x̄, where the value of x̄ provides a point estimate of μ. The standard deviation of a sampling distribution is called the standard error. | |
C3513 | The exponential moving average (EMA) is a technical chart indicator that tracks the price of an investment (like a stock or commodity) over time. The EMA is a type of weighted moving average (WMA) that gives more weighting or importance to recent price data. | |
C3514 | Bayesian inference is a method of statistical inference in which Bayes' theorem is used to update the probability for a hypothesis as more evidence or information becomes available. Bayesian updating is particularly important in the dynamic analysis of a sequence of data. | |
C3515 | Cross-validation is a technique in which we train our model using the subset of the data-set and then evaluate using the complementary subset of the data-set. The three steps involved in cross-validation are as follows : Reserve some portion of sample data-set. | |
C3516 | Multinomial logistic regression deals with situations where the outcome can have three or more possible types (e.g., "disease A" vs. "disease B" vs. "disease C") that are not ordered. Binary logistic regression is used to predict the odds of being a case based on the values of the independent variables (predictors). | |
C3517 | There are several situation in which the variable we want to explain can take only two possible values. This is typically the case when we want to model the choice of an individual. This is why these models are called binary choice models, because they explain a (0/1) dependent variable. | |
C3518 | Stack and Queuegeeksforgeeks.org - Stack Data Structure.geeksforgeeks.org - Introduction and Array Implementation.tutorialspoint.com - Data Structures Algorithms.cs.cmu.edu - Stacks.cs.cmu.edu - Stacks and Queues.cs.cmu.edu - Stacks and Queues. | |
C3519 | The formula to calculate the test statistic comparing two population means is, Z= ( x - y )/√(σx2/n1 + σy2/n2). In order to calculate the statistic, we must calculate the sample means ( x and y ) and sample standard deviations (σx and σy) for each sample separately. n1 and n2 represent the two sample sizes. | |
C3520 | Positive feedback is the opposite of negative feedback in that encourages a physiological process or amplifies the action of a system. Positive feedback is a cyclic process that can continue to amplify your body's response to a stimulus until a negative feedback response takes over. | |
C3521 | A learning model is a description of the mental and physical mechanisms that are involved in the acquisition of new skills and knowledge and how to engage those those mechanisms to encourage and facilitate learning. Under each of these categories are numerous sub-categories to suit virtually any learning style. | |
C3522 | The geometric distribution is a one-parameter family of curves that models the number of failures before one success in a series of independent trials, where each trial results in either success or failure, and the probability of success in any individual trial is constant. | |
C3523 | If two random variables X and Y are independent, then their covariance Cov(X, Y) = E(XY) − E(X)E(Y) = 0, that is, they are uncorrelated. | |
C3524 | Hierarchical clustering outputs a hierarchy, ie a structure that is more informa ve than the unstructured set of flat clusters returned by k-‐means. Therefore, it is easier to decide on the number of clusters by looking at the dendrogram (see sugges on on how to cut a dendrogram in lab8). | |
C3525 | In deep learning, a convolutional neural network (CNN, or ConvNet) is a class of deep neural networks, most commonly applied to analyzing visual imagery. | |
C3526 | linear_model . LinearRegression. Ordinary least squares Linear Regression. | |
C3527 | Simply put, in any application area where you have lots of heterogeneous or noisy data or anywhere you need a clear understanding of your uncertainty are areas that you can use Bayesian Statistics. | |
C3528 | In statistics, the generalized linear model (GLM) is a flexible generalization of ordinary linear regression that allows for response variables that have error distribution models other than a normal distribution. | |
C3529 | The purpose of singular value decomposition is to reduce a dataset containing a large number of values to a dataset containing significantly fewer values, but which still contains a large fraction of the variability present in the original data. | |
C3530 | By “trend value” I mean exactly that: the background level at a given moment. If it changes while the nature of the fluctuations remains the same, the probability of record-setting extremes will of course change. | |
C3531 | Bounding-box regression is a popular technique to refine or predict localization boxes in recent object detection approaches. Typically, bounding-box regressors are trained to regress from either region proposals or fixed anchor boxes to nearby bounding boxes of a pre-defined target object classes. | |
C3532 | In logic, temporal logic is any system of rules and symbolism for representing, and reasoning about, propositions qualified in terms of time (for example, "I am always hungry", "I will eventually be hungry", or "I will be hungry until I eat something"). | |
C3533 | The Sobel filter is used for edge detection. It works by calculating the gradient of image intensity at each pixel within the image. The result of applying it to a pixel on an edge is a vector that points across the edge from darker to brighter values. | |
C3534 | 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. | |
C3535 | Five Common Types of Sampling Errors. Population Specification Error—This error occurs when the researcher does not understand who they should survey. For example, imagine a survey about breakfast cereal consumption. Sample Frame Error—A frame error occurs when the wrong sub-population is used to select a sample. | |
C3536 | In this module, we have discussed on various data preprocessing methods for Machine Learning such as rescaling, binarizing, standardizing, one hot encoding, and label encoding. | |
C3537 | In statistics and machine learning, the bias–variance tradeoff is the property of a model that the variance of the parameter estimates across samples can be reduced by increasing the bias in the estimated parameters. | |
C3538 | Gradient descent is a first-order iterative optimization algorithm for finding a local minimum of a differentiable function. But if we instead take steps proportional to the positive of the gradient, we approach a local maximum of that function; the procedure is then known as gradient ascent. | |
C3539 | A test statistic is a number calculated by a statistical test. It describes how far your observed data is from the null hypothesis of no relationship between variables or no difference among sample groups. | |
C3540 | Random forest will reduce variance part of error rather than bias part, so on a given training data set decision tree may be more accurate than a random forest. But on an unexpected validation data set, Random forest always wins in terms of accuracy. | |
C3541 | Think of feature columns as the intermediaries between raw data and Estimators. Feature columns are very rich, enabling you to transform a diverse range of raw data into formats that Estimators can use, allowing easy experimentation. In simple words feature column are bridge between raw data and estimator or model. | |
C3542 | Ensemble learning helps improve machine learning results by combining several models. Ensemble methods are meta-algorithms that combine several machine learning techniques into one predictive model in order to decrease variance (bagging), bias (boosting), or improve predictions (stacking). | |
C3543 | Using the entire training set is just using a very large minibatch size, where the size of your minibatch is limited by the amount you spend on data collection, rather than the amount you spend on computation. | |
C3544 | Large numbers are numbers that are significantly larger than those typically used in everyday life, for instance in simple counting or in monetary transactions. Very large numbers often occur in fields such as mathematics, cosmology, cryptography, and statistical mechanics.More items | |
C3545 | Using batch normalisation allows much higher learning rates, increasing the speed at which networks train. Makes weights easier to initialise — Weight initialisation can be difficult, especially when creating deeper networks. Batch normalisation helps reduce the sensitivity to the initial starting weights. | |
C3546 | Gradient descent is an optimization algorithm that's used when training a machine learning model. It's based on a convex function and tweaks its parameters iteratively to minimize a given function to its local minimum. | |
C3547 | a transformation in which measurements on a linear scale are converted into probabilities between 0 and 1. It is given by the formula y = ex/(1 + ex), where x is the scale value and e is the Eulerian number. | |
C3548 | n_estimators : This is the number of trees you want to build before taking the maximum voting or averages of predictions. Higher number of trees give you better performance but makes your code slower. | |
C3549 | So, let's have a look at the most common dataset problems and the ways to solve them.How to collect data for machine learning if you don't have any. Articulate the problem early. Establish data collection mechanisms. Format data to make it consistent. Reduce data. Complete data cleaning. Decompose data. Rescale data.More items• | |
C3550 | 7 Techniques to Handle Imbalanced DataUse the right evaluation metrics. Resample the training set. Use K-fold Cross-Validation in the right way. Ensemble different resampled datasets. Resample with different ratios. Cluster the abundant class. Design your own models. | |
C3551 | Gradient clipping is a technique to prevent exploding gradients in very deep networks, usually in recurrent neural networks. This prevents any gradient to have norm greater than the threshold and thus the gradients are clipped. | |
C3552 | In a supervised learning model, the algorithm learns on a labeled dataset, providing an answer key that the algorithm can use to evaluate its accuracy on training data. An unsupervised model, in contrast, provides unlabeled data that the algorithm tries to make sense of by extracting features and patterns on its own. | |
C3553 | Cluster analysis is applied in many fields such as the natural sciences, the medical sciences, economics, marketing, etc. There are essentially two types of clustering methods: hierarchical algorithms and partioning algorithms. The hierarchical algorithms can be divided into agglomerative and splitting procedures. | |
C3554 | Synset is a special kind of a simple interface that is present in NLTK to look up words in WordNet. Synset instances are the groupings of synonymous words that express the same concept. Some of the words have only one Synset and some have several. | |
C3555 | 1:254:40Suggested clip · 105 secondsFinding the Input of a Function Given the Output - YouTubeYouTubeStart of suggested clipEnd of suggested clip | |
C3556 | 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. | |
C3557 | The repeatability is defined as the closeness of agreement between the results of successive measurements of the same measurand carried out subject to the following conditions: • the same measurement procedure, • | |
C3558 | There are a ton of 'smart' algorithms that assist data scientists do the wizardry. k-Means Clustering is an unsupervised learning algorithm that is used for clustering whereas KNN is a supervised learning algorithm used for classification. | |
C3559 | The Non-Linear Decision Boundary SVM works well when the data points are linearly separable. If the decision boundary is non-liner then SVM may struggle to classify. Observe the below examples, the classes are not linearly separable. SVM has no direct theory to set the non-liner decision boundary models. | |
C3560 | The ways in which they function Another fundamental difference between traditional computers and artificial neural networks is the way in which they function. While computers function logically with a set of rules and calculations, artificial neural networks can function via images, pictures, and concepts. | |
C3561 | Some of the most popular methods for outlier detection are:Z-Score or Extreme Value Analysis (parametric)Probabilistic and Statistical Modeling (parametric)Linear Regression Models (PCA, LMS)Proximity Based Models (non-parametric)Information Theory Models.More items | |
C3562 | The prior is, generally speaking, a probability distribution that expresses one's beliefs about a quantity before some evidence is taken into account. If we restrict ourselves to an ML model, the prior can be thought as of the distribution that is imputed before the model starts to see any data. | |
C3563 | The probability distribution for a random error that is as likely to move the value in either direction is called a Gaussian distribution. Such a distribution is characterized by two parameters, µ the mean or average value, and σ the standard deviation. | |
C3564 | Conditional probability is defined as the likelihood of an event or outcome occurring, based on the occurrence of a previous event or outcome. Conditional probability is calculated by multiplying the probability of the preceding event by the updated probability of the succeeding, or conditional, event. | |
C3565 | The Unsharp Mask filter adjusts the contrast of the edge detail and creates the illusion of a more focused image. | |
C3566 | two independent variables | |
C3567 | The interval scale of measurement is a type of measurement scale that is characterized by equal intervals between scale units. A perfect example of an interval scale is the Fahrenheit scale to measure temperature. For example, suppose it is 60 degrees Fahrenheit on Monday and 70 degrees on Tuesday. | |
C3568 | 1:065:39Suggested clip · 107 secondsMake a Histogram Using Excel's Histogram tool in the Data Analysis YouTubeStart of suggested clipEnd of suggested clip | |
C3569 | Page 1. 1 Order Statistics. Definition The order statistics of a random sample X1,,Xn are the sample values placed in ascending order. They are denoted by X(1),,X(n). The order statistics are random variables that satisfy X(1) ≤ X(2) ≤ ··· ≤ X(n). | |
C3570 | Alternatively, general dimensionality reduction techniques are used such as:Independent component analysis.Isomap.Kernel PCA.Latent semantic analysis.Partial least squares.Principal component analysis.Multifactor dimensionality reduction.Nonlinear dimensionality reduction.More items | |
C3571 | Supervised learning involves some process which trains the algorithm. Topic modeling is a form of unsupervised statistical machine learning. It is like document clustering, only instead of each document belonging to a single cluster or topic, a document can belong to many different clusters or topics. | |
C3572 | In artificial intelligence, an intelligent agent (IA) refers to an autonomous entity which acts, directing its activity towards achieving goals (i.e. it is an agent), upon an environment using observation through sensors and consequent actuators (i.e. it is intelligent). | |
C3573 | There are several approaches to avoiding overfitting in building decision trees. Pre-pruning that stop growing the tree earlier, before it perfectly classifies the training set. Post-pruning that allows the tree to perfectly classify the training set, and then post prune the tree. | |
C3574 | The one-way analysis of variance (ANOVA) is used to determine whether there are any statistically significant differences between the means of three or more independent (unrelated) groups. | |
C3575 | Ensemble learning helps improve machine learning results by combining several models. Ensemble methods are meta-algorithms that combine several machine learning techniques into one predictive model in order to decrease variance (bagging), bias (boosting), or improve predictions (stacking). | |
C3576 | Statistical inference is the process of using data analysis to deduce properties of an underlying distribution of probability. Inferential statistical analysis infers properties of a population, for example by testing hypotheses and deriving estimates. | |
C3577 | Train a neural network with TensorFlowStep 1: Import the data.Step 2: Transform the data.Step 3: Construct the tensor.Step 4: Build the model.Step 5: Train and evaluate the model.Step 6: Improve the model. | |
C3578 | The normal distribution is a continuous probability distribution. This has several implications for probability. The total area under the normal curve is equal to 1. The probability that a normal random variable X equals any particular value is 0. | |
C3579 | Loss value implies how poorly or well a model behaves after each iteration of optimization. An accuracy metric is used to measure the algorithm's performance in an interpretable way. It is the measure of how accurate your model's prediction is compared to the true data. | |
C3580 | Real time processing is usually found in systems that use computer control. This processing method is used when it is essential that the input request is dealt with quickly enough so as to be able to control an output properly. The is called the 'latency'. | |
C3581 | This is important because it ensures that the maximum value of the log of the probability occurs at the same point as the original probability function. Therefore we can work with the simpler log-likelihood instead of the original likelihood. | |
C3582 | The first component is the definition: Two variables are independent when the distribution of one does not depend on the the other. If the probabilities of one variable remains fixed, regardless of whether we condition on another variable, then the two variables are independent. | |
C3583 | The number of input variables or features for a dataset is referred to as its dimensionality. Large numbers of input features can cause poor performance for machine learning algorithms. Dimensionality reduction is a general field of study concerned with reducing the number of input features. | |
C3584 | LSTMs solve the problem using a unique additive gradient structure that includes direct access to the forget gate's activations, enabling the network to encourage desired behaviour from the error gradient using frequent gates update on every time step of the learning process. | |
C3585 | The aggregate opinion of a multiple models is less noisy than other models. In finance, we called it “Diversification” a mixed portfolio of many stocks will be much less variable than just one of the stocks alone. This is also why your models will be better with ensemble of models rather than individual. | |
C3586 | When we calculate probabilities involving one event AND another event occurring, we multiply their probabilities. In some cases, the first event happening impacts the probability of the second event. | |
C3587 | There is a popular method known as elbow method which is used to determine the optimal value of K to perform the K-Means Clustering Algorithm. The basic idea behind this method is that it plots the various values of cost with changing k. As the value of K increases, there will be fewer elements in the cluster. | |
C3588 | In a supervised learning model, the algorithm learns on a labeled dataset, providing an answer key that the algorithm can use to evaluate its accuracy on training data. An unsupervised model, in contrast, provides unlabeled data that the algorithm tries to make sense of by extracting features and patterns on its own. | |
C3589 | The significance level, also denoted as alpha or α, is the probability of rejecting the null hypothesis when it is true. For example, a significance level of 0.05 indicates a 5% risk of concluding that a difference exists when there is no actual difference. | |
C3590 | Learning involves far more than thinking: it involves the whole personality - senses, feelings, intuition, beliefs, values and will. Learning occurs when we are able to: Gain a mental or physical grasp of the subject. Make sense of a subject, event or feeling by interpreting it into our own words or actions. | |
C3591 | The short answer is yes—because most regression models will not perfectly fit the data at hand. If you need a more complex model, applying a neural network to the problem can provide much more prediction power compared to a traditional regression. | |
C3592 | Recurrent Neural Networks (RNNs) are a form of machine learning algorithm that are ideal for sequential data such as text, time series, financial data, speech, audio, video among others. | |
C3593 | Learning rate decay (lrDecay) is a \emph{de facto} technique for training modern neural networks. We provide another novel explanation: an initially large learning rate suppresses the network from memorizing noisy data while decaying the learning rate improves the learning of complex patterns. | |
C3594 | This makes systematic sampling functionally similar to simple random sampling (SRS). However it is not the same as SRS because not every possible sample of a certain size has an equal chance of being chosen (e.g. samples with at least two elements adjacent to each other will never be chosen by systematic sampling). | |
C3595 | Conjoint analysis is a survey-based statistical technique used in market research that helps determine how people value different attributes (feature, function, benefits) that make up an individual product or service. | |
C3596 | You must use the t-distribution table when working problems when the population standard deviation (σ) is not known and the sample size is small (n<30). General Correct Rule: If σ is not known, then using t-distribution is correct. If σ is known, then using the normal distribution is correct. | |
C3597 | A simple test of consistency is that all frequencies should be positive. If any frequency is negative, it means that there is inconsistency in the sample data. If the data is consistent, all the ultimate class frequencies will be positive. | |
C3598 | : a function (such as y = loga x or y = ln x) that is the inverse of an exponential function (such as y = ax or y = ex) so that the independent variable appears in a logarithm. | |
C3599 | : a function of a set of variables that is evaluated for samples of events or objects and used as an aid in discriminating between or classifying them. |
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