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C10300 | Accuracy is the percentage of correctly classifies instances out of all instances. Kappa or Cohen's Kappa is like classification accuracy, except that it is normalized at the baseline of random chance on your dataset. | |
C10301 | 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. | |
C10302 | Prior probability represents what is originally believed before new evidence is introduced, and posterior probability takes this new information into account. A posterior probability can subsequently become a prior for a new updated posterior probability as new information arises and is incorporated into the analysis. | |
C10303 | 12 Tips to boost your multitasking skillsAccept your limits. To better manage task organization, be aware of your limits, especially those you can't control. Distinguish urgent from important. Learn to concentrate. Avoid distractions. Work in blocks of time. Work on related tasks together. Learn to supervise. Plan ahead.More items• | |
C10304 | [′au̇t‚pu̇t ‚yü·nət] (computer science) In computers, a unit which delivers information from the computer to an external device or from internal storage to external storage. | |
C10305 | The precision-recall curve shows the tradeoff between precision and recall for different threshold. A high area under the curve represents both high recall and high precision, where high precision relates to a low false positive rate, and high recall relates to a low false negative rate. | |
C10306 | RELU activation solves this by having a gradient slope of 1, so during backpropagation, there isn't gradients passed back that are progressively getting smaller and smaller. but instead they are staying the same, which is how RELU solves the vanishing gradient problem. | |
C10307 | A nonlinear relationship is a type of relationship between two entities in which change in one entity does not correspond with constant change in the other entity. However, nonlinear entities can be related to each other in ways that are fairly predictable, but simply more complex than in a linear relationship. | |
C10308 | Adaptive learning is one technique for providing personalized learning, which aims to provide efficient, effective, and customized learning paths to engage each student. Adaptive learning systems use a data-driven approach to adjust the path and pace of learning, enabling the delivery of personalized learning at scale. | |
C10309 | Linear models, or regression models, trace the the distribution of the dependent variable (Y) – or some characteristic of the distribution (the mean) – as a function of the independent variables (Xs). This shows the conditional distribution of improvement value. | |
C10310 | A loss function is used to optimize a machine learning algorithm. The loss is calculated on training and validation and its interpretation is based on how well the model is doing in these two sets. An accuracy metric is used to measure the algorithm's performance in an interpretable way. | |
C10311 | The error function reports back the difference between the estimated reward at any given state or time step and the actual reward received. When this is paired with a stimulus that accurately reflects a future reward, the error can be used to associate the stimulus with the future reward. | |
C10312 | Overfitting occurs when a statistical model or machine learning algorithm captures the noise of the data. Specifically, underfitting occurs if the model or algorithm shows low variance but high bias. Underfitting is often a result of an excessively simple model. | |
C10313 | Ridge and lasso regression allow you to regularize ("shrink") coefficients. This means that the estimated coefficients are pushed towards 0, to make them work better on new data-sets ("optimized for prediction"). This allows you to use complex models and avoid over-fitting at the same time. | |
C10314 | Confounding means the distortion of the association between the independent and dependent variables because a third variable is independently associated with both. A causal relationship between two variables is often described as the way in which the independent variable affects the dependent variable. | |
C10315 | We can set a threshold value to classify all the values greater than threshold as 1 and lesser then that as 0. That's how the Y is predicted and we get 'Y-predicted'. The default value for threshold on which we generally get a Confusion Matrix is 0.50. | |
C10316 | I daresay that dimensionality reduction is necessary when we are lacking an acceptable balance between bias and variance. Some learning algorithms have some kind of 'built in' dimensionality reduction like the Relevance Vector Machine or Random Forests (to name two that are widely used). | |
C10317 | Most computational models of supervised learning rely only on labeled training examples, and ignore the possible role of unlabeled data. We present an algorithm and experimental results demonstrating that unlabeled data can significantly improve learning accuracy in certain practical problems. | |
C10318 | Overview. Describe the problem. Data and model. What data did you use to address the question, and how did you do it? Results. In your results section, include any figures and tables necessary to make your case. Conclusion. | |
C10319 | Image processing is a method to perform some operations on an image, to get an enhanced image or to extract some useful information from it. However, to get an optimized workflow and to avoid losing time, it is important to process images after the capture, in a post-processing step. | |
C10320 | Discrete Probability Distributions If a random variable is a discrete variable, its probability distribution is called a discrete probability distribution. An example will make this clear. Suppose you flip a coin two times. | |
C10321 | The primary purpose of Convolution in case of a ConvNet is to extract features from the input image. Convolution preserves the spatial relationship between pixels by learning image features using small squares of input data. | |
C10322 | A non-convex optimization problem is any problem where the objective or any of the constraints are non-convex, as pictured below. Such a problem may have multiple feasible regions and multiple locally optimal points within each region. | |
C10323 | Machine learning is an AI technique where the algorithms are given data and are asked to process without a predetermined set of rules and regulations whereas Predictive analysis is the analysis of historical data as well as existing external data to find patterns and behaviors. | |
C10324 | In statistical mechanics, entropy is an extensive property of a thermodynamic system. It quantifies the number Ω of microscopic configurations (known as microstates) that are consistent with the macroscopic quantities that characterize the system (such as its volume, pressure and temperature). | |
C10325 | A probability sampling method is any method of sampling that utilizes some form of random selection. In order to have a random selection method, you must set up some process or procedure that assures that the different units in your population have equal probabilities of being chosen. | |
C10326 | The idea of ensemble classification is to learn not just one classifier but a set of classifiers, called an ensemble of classifiers, and then to combine their predictions for the classification of unseen instances using some form of voting. | |
C10327 | The range is influenced too much by extreme values. | |
C10328 | Facial recognition is a way of recognizing a human face through technology. A facial recognition system uses biometrics to map facial features from a photograph or video. It compares the information with a database of known faces to find a match. | |
C10329 | Multidimensional scaling (MDS) is a means of visualizing the level of similarity of individual cases of a dataset. MDS is used to translate "information about the pairwise 'distances' among a set of n objects or individuals" into a configuration of n points mapped into an abstract Cartesian space. | |
C10330 | Both can learn and become expert in an area and both are mortal. The main difference is, humans can forget but neural networks cannot. Once fully trained, a neural net will not forget. Whatever a neural network learns is hard-coded and becomes permanent. | |
C10331 | Convolution is a mathematical way of combining two signals to form a third signal. It is the single most important technique in Digital Signal Processing. Using the strategy of impulse decomposition, systems are described by a signal called the impulse response. | |
C10332 | Weight is the parameter within a neural network that transforms input data within the network's hidden layers. A neural network is a series of nodes, or neurons. Within each node is a set of inputs, weight, and a bias value. | |
C10333 | A probability event can be defined as a set of outcomes of an experiment. Thus, an event is a subset of the sample space, i.e., E is a subset of S. There could be a lot of events associated with a given sample space. For any event to occur, the outcome of the experiment must be an element of the set of event E. | |
C10334 | Abstract. A major goal of unsupervised learning is to discover data representations that are useful for subsequent tasks, without access to supervised labels during training. It also generalizes to train on data with randomly permuted input dimensions and even generalizes from image datasets to a text task. | |
C10335 | Implementing Deep Learning Methods and Feature Engineering for Text Data: FastText. Overall, FastText is a framework for learning word representations and also performing robust, fast and accurate text classification. The framework is open-sourced by Facebook on GitHub. | |
C10336 | We can compare the quality of two estimators by looking at the ratio of their MSE. If the two estimators are unbiased this is equivalent to the ratio of the variances which is defined as the relative efficiency. rndr = n + 1 n · n n + 1 θ. | |
C10337 | In logistic regression, a set of observations whose values deviate from the expected range and produce extremely large residuals and may indicate a sample peculiarity is called outliers. These outliers can unduly influence the results of the analysis and lead to incorrect inferences. | |
C10338 | R-squared (R2) is a statistical measure that represents the proportion of the variance for a dependent variable that's explained by an independent variable or variables in a regression model. So, if the R2 of a model is 0.50, then approximately half of the observed variation can be explained by the model's inputs. | |
C10339 | Definition: Entropy is a measure of uncertainty of a random variable. The entropy of a discrete random variable X with alphabet X is H(X) = -) p(x) log p(2) DEX When the base of the logarithm is 2, entropy is measured in bits. (Note: you can prove this by assigning a variable pi to the probability of outcome i. | |
C10340 | A Hash Value (also called as Hashes or Checksum) is a string value (of specific length), which is the result of calculation of a Hashing Algorithm. Hash Values have different uses. | |
C10341 | The asymptotic variance-covariance matrix can be used to calculate confidence intervals and to test hypotheses about the variance components. In this example, the variance for the estimated Var(STOREID) is 65787.226. The positive square root of this number gives the standard error for Var(STOREID), which is 256.49. | |
C10342 | Deep Reinforcement Learning: From Toys to Enteprise When paired with simulations, reinforcement learning is a powerful tool for training AI models that can help increase automation or optimize operational efficiency of sophisticated systems such as robotics, manufacturing, and supply chain logistics. | |
C10343 | Most model-performance measures are based on the comparison of the model's predictions with the (known) values of the dependent variable in a dataset. For an ideal model, the predictions and the dependent-variable values should be equal. In practice, it is never the case, and we want to quantify the disagreement. | |
C10344 | The calibration module allows you to better calibrate the probabilities of a given model, or to add support for probability prediction. Well calibrated classifiers are probabilistic classifiers for which the output of the predict_proba method can be directly interpreted as a confidence level. | |
C10345 | Machine bias is the effect of erroneous assumptions in machine learning processes. Bias reflects problems related to the gathering or use of data, where systems draw improper conclusions about data sets, either because of human intervention or as a result of a lack of cognitive assessment of data. | |
C10346 | The pre-attention phase is an automatic process which happens unconsciously. The second stage is focused attention in which an individual takes all of the observed features and combines them to make a complete perception. This second stage process occurs if the object doesn't stand out immediately. | |
C10347 | The value of the z-score tells you how many standard deviations you are away from the mean. If a z-score is equal to 0, it is on the mean. A positive z-score indicates the raw score is higher than the mean average. For example, if a z-score is equal to +1, it is 1 standard deviation above the mean. | |
C10348 | For quick and visual identification of a normal distribution, use a QQ plot if you have only one variable to look at and a Box Plot if you have many. Use a histogram if you need to present your results to a non-statistical public. As a statistical test to confirm your hypothesis, use the Shapiro Wilk test. | |
C10349 | Selection bias is the term used to describe the situation where an analysis has been conducted among a subset of the data (a sample) with the goal of drawing conclusions about the population, but the resulting conclusions will likely be wrong (biased), because the subgroup differs from the population in some important | |
C10350 | Gradient boosting is a machine learning technique for regression and classification problems, which produces a prediction model in the form of an ensemble of weak prediction models, typically decision trees. Explicit regression gradient boosting algorithms were subsequently developed by Jerome H. | |
C10351 | Canonical discriminant analysis is a dimension-reduction technique related to principal component analysis and canonical correlation. This maximal multiple correlation is called the first canonical correlation. The coefficients of the linear combination are the canonical coefficients or canonical weights. | |
C10352 | 3 neurons | |
C10353 | An artificial neural network is an attempt to simulate the network of neurons that make up a human brain so that the computer will be able to learn things and make decisions in a humanlike manner. ANNs are created by programming regular computers to behave as though they are interconnected brain cells. | |
C10354 | Support Vector Machine can also be used as a regression method, maintaining all the main features that characterize the algorithm (maximal margin). The Support Vector Regression (SVR) uses the same principles as the SVM for classification, with only a few minor differences. | |
C10355 | In a standard normal distribution (with mean 0 and standard deviation 1), the first and third quartiles are located at -0.67448 and +0.67448 respectively. Thus the interquartile range (IQR) is 1.34896. | |
C10356 | 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. | |
C10357 | Consider the normal distribution N(100, 10). To find the percentage of data below 105.3, that is P(x < 105.3), standartize first: P(x < 105.3) = P ( z < 105.3 − 100 10 ) = P(z < 0.53). Then find the proportion corresponding to 0.53 in Table A: look for the intersection of the row labeled 0.5 and the column labeled . | |
C10358 | The greater the value, the higher the weight for that feature. The Formula! The Weighted Mean Center is calculated by multiplying the x and y coordinate by the weight for that feature and summing all for both x and y individually, and then dividing this by the sum of all the weights. | |
C10359 | Independent EventsTwo events A and B are said to be independent if the fact that one event has occurred does not affect the probability that the other event will occur.If whether or not one event occurs does affect the probability that the other event will occur, then the two events are said to be dependent. | |
C10360 | The AUC for the ROC can be calculated using the roc_auc_score() function. Like the roc_curve() function, the AUC function takes both the true outcomes (0,1) from the test set and the predicted probabilities for the 1 class. It returns the AUC score between 0.0 and 1.0 for no skill and perfect skill respectively. | |
C10361 | Systematic sampling is easier to do than random sampling. In systematic sampling, the list of elements is "counted off". That is, every kth element is taken. Stratified sampling also divides the population into groups called strata. | |
C10362 | To conduct a stratified analysis we can identify six major steps which have a specific chronology:Conduct a crude analysis. Identify the potential effect modifiers or confounding factors. Measure the effect of exposure on outcome within each stratum. Look for effect modification. Look for confounding.More items• | |
C10363 | Algorithms are methods or procedures taken in other to get a task done or solve a problem, while Models are well-defined computations formed as a result of an algorithm that takes some value, or set of values, as input and produces some value, or set of values as output. | |
C10364 | 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. | |
C10365 | 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. | |
C10366 | Modes, medians, and frequencies are the appropriate statistical tools to use. If you have designed a series of questions that when combined measure a particular trait, you have created a Likert scale. Use means and standard deviations to describe the scale. | |
C10367 | You can improve your pattern recognition skills by practising. Now you know that patterns can appear in numbers, objects, symbols, music and more, you can pay attention to this. Looking and listening while being aware that there are patterns in things most of the time, helps you to eventually find them easier. | |
C10368 | Non-probability sampling is often used because the procedures used to select units for inclusion in a sample are much easier, quicker and cheaper when compared with probability sampling. This is especially the case for convenience sampling. | |
C10369 | If the static shape is not fully defined, the dynamic shape of a Tensor t can be determined by evaluating tf. shape(t) . On the other hand you can extract the static shape by using x. get_shape(). | |
C10370 | A confidence interval is a range of values that is likely to contain an unknown population parameter. If you draw a random sample many times, a certain percentage of the confidence intervals will contain the population mean. This percentage is the confidence level. | |
C10371 | Local Outlier Factor (LOF) The local outlier factor [43] is the most well-known local anomaly detection algorithm and also introduced the idea of local anomalies first. Today, its idea is carried out in many nearest-neighbor based algorithms, such as in the ones described below. | |
C10372 | Edge detection is an image processing technique for finding the boundaries of objects within images. It works by detecting discontinuities in brightness. Edge detection is used for image segmentation and data extraction in areas such as image processing, computer vision, and machine vision. | |
C10373 | A neural network is a series of algorithms that endeavors to recognize underlying relationships in a set of data through a process that mimics the way the human brain operates. Neural networks can adapt to changing input; so the network generates the best possible result without needing to redesign the output criteria. | |
C10374 | Random forest is a supervised learning algorithm. The "forest" it builds, is an ensemble of decision trees, usually trained with the “bagging” method. The general idea of the bagging method is that a combination of learning models increases the overall result. | |
C10375 | Step 1: Divide your confidence level by 2: .95/2 = 0.475. Step 2: Look up the value you calculated in Step 1 in the z-table and find the corresponding z-value. The z-value that has an area of .475 is 1.96. Step 3: Divide the number of events by the number of trials to get the “P-hat” value: 24/160 = 0.15. | |
C10376 | Time efficiency - a measure of amount of time for an algorithm to execute. Space efficiency - a measure of the amount of memory needed for an algorithm to execute. Asymptotic dominance - comparison of cost functions when n is large. That is, g asymptotically dominates f if g dominates f for all "large" values of n. | |
C10377 | The area under the normal curve is equal to 1.0. Normal distributions are denser in the center and less dense in the tails. Normal distributions are defined by two parameters, the mean (μ) and the standard deviation (σ). 68% of the area of a normal distribution is within one standard deviation of the mean. | |
C10378 | The hazard function is the instantaneous rate of failure at a given time. Characteristics of a hazard function are frequently associated with certain products and applications. Different hazard functions are modeled with different distribution models. | |
C10379 | 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. | |
C10380 | ReLu bounded negative outputs to 0 & above. This works well in hidden layers than the final output layer. It is not typical, since in this case, the ouput value is not bounded in a range. | |
C10381 | Root Mean Square Error (RMSE) is the standard deviation of the residuals (prediction errors). Residuals are a measure of how far from the regression line data points are; RMSE is a measure of how spread out these residuals are. In other words, it tells you how concentrated the data is around the line of best fit. | |
C10382 | For a discrete random variable, x, the probability distribution is defined by a probability mass function, denoted by f(x). This function provides the probability for each value of the random variable. | |
C10383 | Use imputation for the missing values. When the response is missing, we can use a predictive model to predict the missing response, then create a new fully-observed dataset containing the predictions instead of the missing values, and finally re-estimate the predictive model in this expanded dataset. | |
C10384 | Correlation is a statistical technique that can show whether and how strongly pairs of variables are related. For example, height and weight are related; taller people tend to be heavier than shorter people. An intelligent correlation analysis can lead to a greater understanding of your data. | |
C10385 | Therefore, a low test–retest reliability correlation might be indicative of a measure with low reliability, of true changes in the persons being measured, or both. That is, in the test–retest method of estimating reliability, it is not possible to separate the reliability of measure from its stability. | |
C10386 | Batch gradient descent computes the gradient using the whole dataset. This is great for convex, or relatively smooth error manifolds. In this case, we move somewhat directly towards an optimum solution, either local or global. Stochastic gradient descent (SGD) computes the gradient using a single sample. | |
C10387 | A correlation coefficient is a numerical measure of some type of correlation, meaning a statistical relationship between two variables. | |
C10388 | The problem is that probability and odds have different properties that give odds some advantages in statistics. For example, in logistic regression the odds ratio represents the constant effect of a predictor X, on the likelihood that one outcome will occur. | |
C10389 | A random walk on a graph is a very special case of a Markov chain. Unlike a general Markov chain, random walk on a graph enjoys a property called time symmetry or reversibility. | |
C10390 | We will run the ANOVA using the five-step approach.Set up hypotheses and determine level of significance. H0: μ1 = μ2 = μ3 = μ4 H1: Means are not all equal α=0.05.Select the appropriate test statistic. Set up decision rule. Compute the test statistic. Conclusion. | |
C10391 | When the null hypothesis is true and you reject it, you make a type I error. The probability of making a type I error is α, which is the level of significance you set for your hypothesis test. An α of 0.05 indicates that you are willing to accept a 5% chance that you are wrong when you reject the null hypothesis. | |
C10392 | In statistics, a two-tailed test is a method in which the critical area of a distribution is two-sided and tests whether a sample is greater or less than a range of values. If the sample being tested falls into either of the critical areas, the alternative hypothesis is accepted instead of the null hypothesis. | |
C10393 | 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• | |
C10394 | The T distribution is similar to the normal distribution, just with fatter tails. Both assume a normally distributed population. T distributions have higher kurtosis than normal distributions. The probability of getting values very far from the mean is larger with a T distribution than a normal distribution. | |
C10395 | We can use the regression line to predict a value of "Y" for any "X" score. The steepness of the angle of the regression line is called its slope. It is the amount of change in "Y" that we can expect for any unit change in "X". | |
C10396 | Variance (σ2) in statistics is a measurement of the spread between numbers in a data set. That is, it measures how far each number in the set is from the mean and therefore from every other number in the set. | |
C10397 | Autocorrelation represents the degree of similarity between a given time series and a lagged version of itself over successive time intervals. Autocorrelation measures the relationship between a variable's current value and its past values. | |
C10398 | Applications and considerations. n-gram models are widely used in statistical natural language processing. In speech recognition, phonemes and sequences of phonemes are modeled using a n-gram distribution. For parsing, words are modeled such that each n-gram is composed of n words. | |
C10399 | Improving recall involves adding more accurately tagged text data to the tag in question. In this case, you are looking for the texts that should be in this tag but are not, or were incorrectly predicted (False Negatives). The best way to find these kind of texts is to search for them using keywords. |
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