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C3100 | The Slovin's Formula is given as follows: n = N/(1+Ne2), where n is the sample size, N is the population size and e is the margin of error to be decided by the researcher. | |
C3101 | Definition. The class intervals are the subsets into which the data is grouped. The width of the class intervals will be a compromise between having intervals short enough so that not all of the observations fall in the same interval, but long enough so that you do not end up with only one observation per interval. | |
C3102 | In Regression Clustering (RC), K (>1) regression functions are applied to the dataset simultaneously which guide the clustering of the dataset into K subsets each with a simpler distribution matching its guiding function. Each function is regressed on its own subset of data with a much smaller residue error. | |
C3103 | Binary Variables A simple version of a categorical variable is called a binary variable. This type of variable lists two distinct, mutually exclusive choices. True-or-false and yes-or-no questions are examples of binary variables. | |
C3104 | Data Analytics is a Bigger picture of the same thing which is referred as Machine learning. Like Data Analytics has various categories based on the Data used, similarly, Machine Learning, expresses the way one machine learns a code or work in supervised,unsupervised,semi supervised and reinforcement manner. | |
C3105 | edit: More explanation - sigma basically controls how "fat" your kernel function is going to be; higher sigma values blur over a wider radius. Since you're working with images, bigger sigma also forces you to use a larger kernel matrix to capture enough of the function's energy. | |
C3106 | The conversion of a frequency distribution to a probability distribution is also called an adjusted histogram. This is true for continuous random variables. To convert a frequency distribution to a probability distribution, divide area of the bar or interval of x by the total area of all the Bars. | |
C3107 | Loss curves are a standard actuarial technique for helping insurance companies assess the amount of reserve capital they need to keep on hand to cover claims from a line of business. Claims made and reported for a given accounting period are tracked seperately over time. | |
C3108 | the state of being likely or probable; probability. a probability or chance of something: There is a strong likelihood of his being elected. | |
C3109 | (When does a random variable have a Poisson YouTubeStart of suggested clipEnd of suggested clip | |
C3110 | CRF is a discriminant model. MEMM is not a generative model, but a model with finite states based on state classification. HMM and MEMM are a directed graph, while CRF is an undirected graph. HMM directly models the transition probability and the phenotype probability, and calculates the probability of co-occurrence. | |
C3111 | In the frequency or Fourier domain, the value and location are represented by sinusoidal relationships that depend upon the frequency of a pixel occurring within an image. In this domain, pixel location is represented by its x- and y-frequencies and its value is represented by an amplitude. | |
C3112 | Binomial Approximation The normal distribution can be used as an approximation to the binomial distribution, under certain circumstances, namely: If X ~ B(n, p) and if n is large and/or p is close to ½, then X is approximately N(np, npq) | |
C3113 | A Sampling unit is one of the units selected for the purpose of sampling. Each unit being regarded as individual and indivisible when the selection is made. CONTEXT: Many times the Sampling frame and the Sampling unit are derived from Administrative data. | |
C3114 | After you collect the data, one way to check whether your data are random is to use a runs test to look for a pattern in your data over time. To perform a runs test in Minitab, choose Stat > Nonparametrics > Runs Test. There are also other graphs that can identify whether a sample is random. | |
C3115 | In the Stepwise regression technique, we start fitting the model with each individual predictor and see which one has the lowest p-value. Then pick that variable and then fit the model using two variable one which we already selected in the previous step and taking one by one all remaining ones. | |
C3116 | In this paper, we propose the "adversarial autoencoder" (AAE), which is a probabilistic autoencoder that uses the recently proposed generative adversarial networks (GAN) to perform variational inference by matching the aggregated posterior of the hidden code vector of the autoencoder with an arbitrary prior | |
C3117 | Artificial intelligence is a technology which enables a machine to simulate human behavior. Machine learning is a subset of AI which allows a machine to automatically learn from past data without programming explicitly. The goal of AI is to make a smart computer system like humans to solve complex problems. | |
C3118 | This tutorial is divided into four parts; they are:Regression Dataset.Numerical Feature Selection. Correlation Feature Selection. Mutual Information Feature Selection.Modeling With Selected Features. Model Built Using All Features. Model Built Using Correlation Features. Tune the Number of Selected Features. | |
C3119 | Last Updated on Septem. Multioutput regression are regression problems that involve predicting two or more numerical values given an input example. An example might be to predict a coordinate given an input, e.g. predicting x and y values. | |
C3120 | Introduction Statistical discrete processes – for example, the number of accidents per driver, the number of insects per leaf in an orchard, the number of thunderstorms per year, the number of earthquakes per year, the number of patients visit emergency room in a certain hospital per day - often occur in real life. | |
C3121 | Try to see the difference between an estimator and an estimate. An estimator is a random variable and an estimate is a number (that is the computed value of the estimator). Similarly, the sample median would be a natural point estimator for the population median. | |
C3122 | Multivariate analysis is conceptualized by tradition as the statistical study of experiments in which multiple measurements are made on each experimental unit and for which the relationship among multivariate measurements and their structure are important to the experiment's understanding. | |
C3123 | Object is a copy of the class. Instance is a variable that holds the memory address of the object. You can also have multiple objects of the same class and then multiple instances of each of those objects. In these cases, each object's set of instances are equivalent in value, but the instances between objects are not. | |
C3124 | There are two stages to prediction. The first stage is training the model—this is where the tree is built, tested, and optimized by using an existing collection of data. In the second stage, you actually use the model to predict an unknown outcome. | |
C3125 | Joint probability is calculated by multiplying the probability of event A, expressed as P(A), by the probability of event B, expressed as P(B). For example, suppose a statistician wishes to know the probability that the number five will occur twice when two dice are rolled at the same time. | |
C3126 | It is a primary goal of some artificial intelligence research and a common topic in science fiction and futures studies. AGI can also be referred to as strong AI, full AI, or general intelligent action. Some academic sources reserve the term "strong AI" for machines that can experience consciousness. | |
C3127 | 5.2 RIPPER. The next algorithm we used, RIPPER [3], is an inductive rule learner. This algorithm used libBFD information as features. RIPPER is a rule-based learner that builds a set of rules that identify the classes while minimizing the amount of error. | |
C3128 | Today, neural networks are used for solving many business problems such as sales forecasting, customer research, data validation, and risk management. For example, at Statsbot we apply neural networks for time-series predictions, anomaly detection in data, and natural language understanding. | |
C3129 | 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. | |
C3130 | Linear programming is a special case of mathematical programming (mathematical optimization). Now linear programming is a subset of machine learning known as supervised learning. In a supervised learning, the system knows the patterns and the pattern is well defined based on previous data and information. | |
C3131 | Lasso regression stands for Least Absolute Shrinkage and Selection Operator. The difference between ridge and lasso regression is that it tends to make coefficients to absolute zero as compared to Ridge which never sets the value of coefficient to absolute zero. | |
C3132 | A decision tree is a non-linear classifier. If your dataset contains consistent samples, namely you don't have the same input features and contradictory labels, decision trees can classify the data entirely and overfit it. | |
C3133 | The ReLU activation solves the problem of vanishing gradient that is due to sigmoid-like non-linearities (the gradient vanishes because of the flat regions of the sigmoid). The other kind of "vanishing" gradient seems to be related to the depth of the network (e.g. see this for example). | |
C3134 | AdaBoost is one of the first boosting algorithms to be adapted in solving practices. Adaboost helps you combine multiple “weak classifiers” into a single “strong classifier”. → AdaBoost algorithms can be used for both classification and regression problem. | |
C3135 | Compare the P-value to the α significance level stated earlier. If it is less than α, reject the null hypothesis. If the result is greater than α, fail to reject the null hypothesis. If you reject the null hypothesis, this implies that your alternative hypothesis is correct, and that the data is significant. | |
C3136 | Logistic regression models are a great tool for analysing binary and categorical data, allowing you to perform a contextual analysis to understand the relationships between the variables, test for differences, estimate effects, make predictions, and plan for future scenarios. | |
C3137 | Categorical data works well with Decision Trees, while continuous data work well with Logistic Regression. If your data is categorical, then Logistic Regression cannot handle pure categorical data (string format). Therefore, if you have lots of categorical data, go with a Decision Tree. | |
C3138 | The low-pass filter has a gain response with a frequency range from zero frequency (DC) to ωC. Any input that has a frequency below the cutoff frequency ωC gets a pass, and anything above it gets attenuated or rejected. The gain approaches zero as frequency increases to infinity. | |
C3139 | In probability theory, a continuity correction is an adjustment that is made when a discrete distribution is approximated by a continuous distribution. | |
C3140 | The seeds of modern AI were planted by classical philosophers who attempted to describe the process of human thinking as the mechanical manipulation of symbols. This work culminated in the invention of the programmable digital computer in the 1940s, a machine based on the abstract essence of mathematical reasoning. | |
C3141 | You should put it after the non-linearity (eg. relu layer). If you are using dropout remember to use it before. | |
C3142 | Given two random variables X and Y, the correlation is scale and location invariant in the sense that cor(X,Y)=cor(XT,YT), if XT=a+bX, and YT=c+dY, and b and d have the same sign (either both positive or both negative). | |
C3143 | Technically, the probability density of variable X , means the probability per unit increment of X . The units of probability density are the reciprocal of the units of X — if the units of X are dollars, the units of probability density are probability per dollar increment. | |
C3144 | 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. | |
C3145 | The Bayes theorem describes the probability of an event based on the prior knowledge of the conditions that might be related to the event. If we know the conditional probability , we can use the bayes rule to find out the reverse probabilities . | |
C3146 | The Formula for the Slope For paired data (x,y) we denote the standard deviation of the x data by sx and the standard deviation of the y data by sy. The formula for the slope a of the regression line is: a = r(sy/sx) | |
C3147 | Logistic regression is a statistical analysis method used to predict a data value based on prior observations of a data set. Based on historical data about earlier outcomes involving the same input criteria, it then scores new cases on their probability of falling into a particular outcome category. | |
C3148 | The loss is calculated on training and validation and its interpretation is how well the model is doing for these two sets. Unlike accuracy, a loss is not a percentage. It is a sum of the errors made for each example in training or validation sets. | |
C3149 | The function scipy. linalg. eig computes eigenvalues and eigenvectors of a square matrix . | |
C3150 | Simply put, a z-score (also called a standard score) gives you an idea of how far from the mean a data point is. But more technically it's a measure of how many standard deviations below or above the population mean a raw score is. A z-score can be placed on a normal distribution curve. | |
C3151 | Categorical variables require special attention in regression analysis because, unlike dichotomous or continuous variables, they cannot by entered into the regression equation just as they are. Instead, they need to be recoded into a series of variables which can then be entered into the regression model. | |
C3152 | Definition: An image processing method that creates a bitonal (aka binary) image based on setting a threshold value on the pixel intensity of the original image. The thresholding process is sometimes described as separating an image into foreground values (black) and background values (white). | |
C3153 | Adam is a replacement optimization algorithm for stochastic gradient descent for training deep learning models. Adam combines the best properties of the AdaGrad and RMSProp algorithms to provide an optimization algorithm that can handle sparse gradients on noisy problems. | |
C3154 | A loss function is a measure of how good a prediction model does in terms of being able to predict the expected outcome. A most commonly used method of finding the minimum point of function is “gradient descent”. Loss functions can be broadly categorized into 2 types: Classification and Regression Loss. | |
C3155 | Definition. In machine learning, model validation is referred to as the process where a trained model is evaluated with a testing data set. The testing data set is a separate portion of the same data set from which the training set is derived. Model validation is carried out after model training. | |
C3156 | Batch normalization is a layer that allows every layer of the network to do learning more independently. It is used to normalize the output of the previous layers. The activations scale the input layer in normalization. | |
C3157 | In image processing, a Gaussian blur (also known as Gaussian smoothing) is the result of blurring an image by a Gaussian function (named after mathematician and scientist Carl Friedrich Gauss). It is a widely used effect in graphics software, typically to reduce image noise and reduce detail. | |
C3158 | 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. | |
C3159 | We propose that especially in the context of introducing automated decision aids to explicitly reduce human error, people become primed to use decision aids in biased ways. Rather than necessarily leading to fewer errors, automated decision aids may simply lead to di!erent kinds or classes of errors. | |
C3160 | Random Forest is intrinsically suited for multiclass problems, while SVM is intrinsically two-class. For multiclass problem you will need to reduce it into multiple binary classification problems. Random Forest works well with a mixture of numerical and categorical features. | |
C3161 | In mathematics (in particular, functional analysis), convolution is a mathematical operation on two functions (f and g) that produces a third function ( ) that expresses how the shape of one is modified by the other. The term convolution refers to both the result function and to the process of computing it. | |
C3162 | In statistics, the p-value is the probability of obtaining results at least as extreme as the observed results of a statistical hypothesis test, assuming that the null hypothesis is correct. A smaller p-value means that there is stronger evidence in favor of the alternative hypothesis. | |
C3163 | load_data function Loads the MNIST dataset. This is a dataset of 60,000 28x28 grayscale images of the 10 digits, along with a test set of 10,000 images. More info can be found at the MNIST homepage. | |
C3164 | Results of a study can be made more accurate by controlling for the variation in the covariate. So, a covariate is in fact, a type of control variable. A control variable is a nominal variable (not continuous) and although it has more than one value, the values are categorical and not infinite. | |
C3165 | 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. | |
C3166 | Creating A Target VariableFrom the menu: Click View > User Variables. The Variables dialog box appears. Click Add Target.From the Target pane: Right-click a linked field and select Edit Lookup Criteria. The Edit Lookup Criteria for the selected field appears. Click Edit Lookup Formula. The Edit Formula for the selected field appears. | |
C3167 | Bootstrapping is a type of resampling where large numbers of smaller samples of the same size are repeatedly drawn, with replacement, from a single original sample. | |
C3168 | A pair of computer scientists have created a neural network that can self-replicate. “Self-replication is a key aspect of biological life that has been largely overlooked in Artificial Intelligence systems,” they argue in a paper popped onto arXiv this month. | |
C3169 | In artificial intelligence and operations research, constraint satisfaction is the process of finding a solution to a set of constraints that impose conditions that the variables must satisfy. Constraint propagation methods are also used in conjunction with search to make a given problem simpler to solve. | |
C3170 | Specificity (True negative rate) Specificity (SP) is calculated as the number of correct negative predictions divided by the total number of negatives. It is also called true negative rate (TNR). The best specificity is 1.0, whereas the worst is 0.0. | |
C3171 | How to Calculate a Confusion MatrixStep 1) First, you need to test dataset with its expected outcome values.Step 2) Predict all the rows in the test dataset.Step 3) Calculate the expected predictions and outcomes: | |
C3172 | Use Simple Random Sampling One of the most effective methods that can be used by researchers to avoid sampling bias is simple random sampling, in which samples are chosen strictly by chance. This provides equal odds for every member of the population to be chosen as a participant in the study at hand. | |
C3173 | Because the standard normal distribution is used to calculate critical values for the test, this test is often called the one-sample z-test. | |
C3174 | The Sobel operator, sometimes called the Sobel–Feldman operator or Sobel filter, is used in image processing and computer vision, particularly within edge detection algorithms where it creates an image emphasising edges. | |
C3175 | How TensorFlow works. TensorFlow allows developers to create dataflow graphs—structures that describe how data moves through a graph, or a series of processing nodes. Each node in the graph represents a mathematical operation, and each connection or edge between nodes is a multidimensional data array, or tensor. | |
C3176 | Tensorflow is the most popular and apparently best Deep Learning Framework out there. Tensorflow can be used to achieve all of these applications. The reason for its popularity is the ease with which developers can build and deploy applications. | |
C3177 | Logarithm, the exponent or power to which a base must be raised to yield a given number. Expressed mathematically, x is the logarithm of n to the base b if bx = n, in which case one writes x = logb n. For example, 23 = 8; therefore, 3 is the logarithm of 8 to base 2, or 3 = log2 8. | |
C3178 | 1 Answer. In word2vec, you train to find word vectors and then run similarity queries between words. In doc2vec, you tag your text and you also get tag vectors. If two authors generally use the same words then their vector will be closer. | |
C3179 | A statistical hypothesis is a formal claim about a state of nature structured within the framework of a statistical model. For example, one could claim that the median time to failure from (acce]erated) electromigration of the chip population described in Section 6.1. | |
C3180 | Normalization is a systematic approach of decomposing tables to eliminate data redundancy(repetition) and undesirable characteristics like Insertion, Update and Deletion Anomalies. It is a multi-step process that puts data into tabular form, removing duplicated data from the relation tables. | |
C3181 | Probability RulesEvery probability is between zero and one. In other words, if A is an event, then 0≤P(A)≤1.The sum of the probabilities of all of the outcomes is one. In other words, if all of the outcomes in the sample space are denoted by Ai, then ∑Ai=1.Impossible events have probability zero. Certain events have probability one. | |
C3182 | A linear regression model attempts to explain the relationship between two or more variables using a straight line. Consider the data obtained from a chemical process where the yield of the process is thought to be related to the reaction temperature (see the table below). | |
C3183 | Conclusion – Standard Deviation vs Mean Standard deviation is the deviation from the mean, and a standard deviation is nothing but the square root of the variance. Mean is an average of all set of data available with an investor or company. | |
C3184 | Anyhow, you could start by reading Introduction to Artificial Neural Networks by Jacek M. Zurada. It's a very good and tells you all you need to know about neural networks. Try to follow the Machine Learning course offered by Stanford on Coursera. | |
C3185 | Active learning is an approach to instruction that involves actively engaging students with the course material through discussions, problem solving, case studies, role plays and other methods. | |
C3186 | Gaussian smoothing filters are commonly used to reduce noise. Gaussian filters are generally isotropic, that is, they have the same standard deviation along both dimensions. An image can be filtered by an isotropic Gaussian filter by specifying a scalar value for sigma . | |
C3187 | In your case, with three groups, you'd run ANOVA. If you need to compare the 5-point scales one at a time, then non-parametric statistics are more appropriate. To compare two groups use the Mann-Whitney U test. To compare three or more groups use the Kruskal–Wallis H test. | |
C3188 | The odds ratio tells us how much higher the odds of exposure are among case-patients than among controls. An odds ratio of • 1.0 (or close to 1.0) indicates that the odds of exposure among case-patients are the same as, or similar to, the odds of exposure among controls. The exposure is not associated with the disease. | |
C3189 | When we run studies we want to be confident in the results from our sample. Confidence intervals show us the likely range of values of our population mean. When we calculate the mean we just have one estimate of our metric; confidence intervals give us richer data and show the likely values of the true population mean. | |
C3190 | Prior probability represents what is originally believed before new evidence is introduced, and posterior probability takes this new information into account. | |
C3191 | OLS (linear regression, linear model) assumes normally distributed residuals. Ordinary least squares assumes things like equal variance of the noise at every x location. Generalized least squares does not assume a diagonal co-variance matrix. | |
C3192 | A Classification and Regression Tree(CART) is a predictive algorithm used in machine learning. It explains how a target variable's values can be predicted based on other values. It is a decision tree where each fork is a split in a predictor variable and each node at the end has a prediction for the target variable. | |
C3193 | If you want to control for the effects of some variables on some dependent variable, you just include them into the model. Say, you make a regression with a dependent variable y and independent variable x. You think that z has also influence on y too and you want to control for this influence. | |
C3194 | 5 Ways to Avoid Being Fooled By Statistics. Do A Little Bit of Math and apply Common Sense. Always Look for the Source and check the authority of the source. Question if the statistics are biased or statistically insignificant. Question if the statistics are skewed purposely or Misinterpreted.More items• | |
C3195 | The input() method reads a line from the input (usually from the user), converts the line into a string by removing the trailing newline, and returns it. | |
C3196 | In predictive analytics and machine learning, the concept drift means that the statistical properties of the target variable, which the model is trying to predict, change over time in unforeseen ways. The term concept refers to the quantity to be predicted. | |
C3197 | A 95% confidence interval for βi has two equivalent definitions: The interval is the set of values for which a hypothesis test to the level of 5% cannot be rejected. The interval has a probability of 95% to contain the true value of βi . | |
C3198 | Big data is a big deal. From reducing their costs and making better decisions, to creating products and services that are in demand by customers, businesses will increasingly benefit by using big-data analytics. | |
C3199 | Add More Layers: If you have a complex dataset, you should utilize the power of deep neural networks and smash on some more layers to your architecture. These additional layers will allow your network to learn a more complex classification function that may improve your classification performance. Add more layers! III. |
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