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C3300 | The data used in cluster analysis can be interval, ordinal or categorical. However, having a mixture of different types of variable will make the analysis more complicated. | |
C3301 | Prediction bias is a quantity that measures how far apart those two averages are. That is: prediction bias = average of predictions − average of labels in data set. Note: "Prediction bias" is a different quantity than bias (the b in wx + b). | |
C3302 | The input X provides the initial information that then propagates to the hidden units at each layer and finally produce the output y^. The architecture of the network entails determining its depth, width, and activation functions used on each layer. Depth is the number of hidden layers. | |
C3303 | A batch size of 32 means that 32 samples from the training dataset will be used to estimate the error gradient before the model weights are updated. | |
C3304 | The determinant is related to the volume of the space occupied by the swarm of data points represented by standard scores on the measures involved. When the measures are correlated, the space occupied becomes an ellipsoid whose volume is less than 1. | |
C3305 | To recap the differences between the two: Machine learning uses algorithms to parse data, learn from that data, and make informed decisions based on what it has learned. Deep learning structures algorithms in layers to create an "artificial neural network” that can learn and make intelligent decisions on its own. | |
C3306 | Nucleus is a library of Python and C++ code designed to make it easy to read, write and analyze data in common genomics file formats like SAM and VCF. A library from DeepMind for constructing neural networks. A learning framework to train neural networks by leveraging structured signals in addition to feature inputs. | |
C3307 | A cost function is something you want to minimize. For example, your cost function might be the sum of squared errors over your training set. Gradient descent is a method for finding the minimum of a function of multiple variables. So you can use gradient descent to minimize your cost function. | |
C3308 | 4:0054:57Suggested clip · 116 secondsMaximum Likelihood Estimation Derivation Properties - YouTubeYouTubeStart of suggested clipEnd of suggested clip | |
C3309 | Factor-Label Method | |
C3310 | Artificial intelligence (AI) is evolving—literally. Researchers have created software that borrows concepts from Darwinian evolution, including “survival of the fittest,” to build AI programs that improve generation after generation without human input. | |
C3311 | In column-oriented NoSQL databases, data is stored in cells grouped in columns of data rather than as rows of data. Columns are logically grouped into column families. Column families can contain a virtually unlimited number of columns that can be created at runtime or while defining the schema. | |
C3312 | ROC is a probability curve and AUC represents degree or measure of separability. It tells how much model is capable of distinguishing between classes. Higher the AUC, better the model is at predicting 0s as 0s and 1s as 1s. | |
C3313 | In some research studies one variable is used to predict or explain differences in another variable. In those cases, the explanatory variable is used to predict or explain differences in the response variable. In an experimental study, the explanatory variable is the variable that is manipulated by the researcher. | |
C3314 | Explanation: The objective of perceptron learning is to adjust weight along with class identification. | |
C3315 | Supervised machine learning builds a model that makes predictions based on evidence in the presence of uncertainty. A supervised learning algorithm takes a known set of input data and known responses to the data (output) and trains a model to generate reasonable predictions for the response to new data. | |
C3316 | An AB test is an example of statistical hypothesis testing, a process whereby a hypothesis is made about the relationship between two data sets and those data sets are then compared against each other to determine if there is a statistically significant relationship or not. | |
C3317 | Accuracy Assessment for Image ClassificationOpen the Create Accuracy Assessment Points tool and set the Target Field to Ground Truth.Select a sampling strategy. Open the Update Accuracy Assessment Points tool.Set the Input Raster or Feature Class data as the classified dataset.More items | |
C3318 | Cross-entropy can be calculated using the probabilities of the events from P and Q, as follows: H(P, Q) = – sum x in X P(x) * log(Q(x)) | |
C3319 | Zero-shot learning aims at predicting a large number of unseen classes using only labeled data from a small set of classes and external knowledge about class relations. Moreover, the number of categories keeps increasing as well as the difficulty to collect new data for each new category. | |
C3320 | You now know that: Bias is the simplifying assumptions made by the model to make the target function easier to approximate. Variance is the amount that the estimate of the target function will change given different training data. Trade-off is tension between the error introduced by the bias and the variance. | |
C3321 | A tensor is a quantity, for example a stress or a strain, which has magnitude, direction, and a plane in which it acts. Stress and strain are both tensor quantities. A tensor is a quantity, for example a stress or a strain, which has magnitude, direction, and a plane in which it acts. | |
C3322 | Cross-validation is a resampling procedure used to evaluate machine learning models on a limited data sample. That is, to use a limited sample in order to estimate how the model is expected to perform in general when used to make predictions on data not used during the training of the model. | |
C3323 | When a data set has a negative value, the axis will be shifted upward by –MIN(R) where R is the data range containing the data. Thus if R ranges from -10 to 20, the range in the chart will range from 0 to 30. | |
C3324 | The distribution margin is an accountancy term that describes the degree of profit or loss with respect to a good that is bought wholesale. You will need the wholesale price of the good, as well as the average sales price for which you are selling the good on the market. | |
C3325 | In the binomial distribution, the number of trials is fixed, and we count the number of "successes". Whereas, in the geometric and negative binomial distributions, the number of "successes" is fixed, and we count the number of trials needed to obtain the desired number of "successes". | |
C3326 | To calculate the standard deviation of those numbers:Work out the Mean (the simple average of the numbers)Then for each number: subtract the Mean and square the result.Then work out the mean of those squared differences.Take the square root of that and we are done! | |
C3327 | Main MenuContinuous Optimization.Bound Constrained Optimization.Constrained Optimization.Derivative-Free Optimization.Discrete Optimization.Global Optimization.Linear Programming.Nondifferentiable Optimization.More items | |
C3328 | One of the drawbacks of SGD is that it uses a common learning rate for all parameters. For optimization problems with huge number of parameters, this might be problematic: Let's say your objective function contours look like the above. | |
C3329 | In sampling with replacement the mean of all sample means equals the mean of the population: Whatever the shape of the population distribution, the distribution of sample means is approximately normal with better approximations as the sample size, n, increases. | |
C3330 | Five Common Types of Sampling ErrorsPopulation Specification Error—This error occurs when the researcher does not understand who they should survey. Sample Frame Error—A frame error occurs when the wrong sub-population is used to select a sample.More items | |
C3331 | If both variables tend to increase or decrease together, the coefficient is positive, and the line that represents the correlation slopes upward. If one variable tends to increase as the other decreases, the coefficient is negative, and the line that represents the correlation slopes downward. | |
C3332 | As the degrees of freedom of a Chi Square distribution increase, the Chi Square distribution begins to look more and more like a normal distribution. Thus, out of these choices, a Chi Square distribution with 10 df would look the most similar to a normal distribution. | |
C3333 | The geometric mean differs from the arithmetic average, or arithmetic mean, in how it is calculated because it takes into account the compounding that occurs from period to period. Because of this, investors usually consider the geometric mean a more accurate measure of returns than the arithmetic mean. | |
C3334 | Regression to the mean is all about how data evens out. It basically states that if a variable is extreme the first time you measure it, it will be closer to the average the next time you measure it. In technical terms, it describes how a random variable that is outside the norm eventually tends to return to the norm. | |
C3335 | Derivative RulesCommon FunctionsFunctionDerivativeSquarex22xSquare Root√x(½)x-½Exponentialexexaxln(a) ax24 more rows | |
C3336 | Logistic regression is a model for binary classification predictive modeling. Under this framework, a probability distribution for the target variable (class label) must be assumed and then a likelihood function defined that calculates the probability of observing the outcome given the input data and the model. | |
C3337 | Epsilon is used when we are selecting specific actions base on the Q values we already have. In conclusion learning rate is associated with how big you take a leap and epsilon is associated with how random you take an action. | |
C3338 | The main difference is the one of focus. Data Engineers are focused on building infrastructure and architecture for data generation. In contrast, data scientists are focused on advanced mathematics and statistical analysis on that generated data. Simply put, data scientists depend on data engineers. | |
C3339 | Stratified random sampling is a method of sampling that involves the division of a population into smaller sub-groups known as strata. In stratified random sampling, or stratification, the strata are formed based on members' shared attributes or characteristics such as income or educational attainment. | |
C3340 | In statistics, the t-statistic is the ratio of the departure of the estimated value of a parameter from its hypothesized value to its standard error. For example, the T-statistic is used in estimating the population mean from a sampling distribution of sample means if the population standard deviation is unknown. | |
C3341 | Univariate analysis, looking at single variables, is typically the first procedure one does when examining first time data. The SPSS tools for looking at single variables include the following procedures: Frequencies, Descriptives and Explore all located under the Analyze menu. | |
C3342 | Operating system is a system software. Kernel is a part of operating system. Operating system acts as an interface between user and hardware. Kernel acts as an interface between applications and hardware. | |
C3343 | TensorFlow is a free and open-source software library for machine learning. It can be used across a range of tasks but has a particular focus on training and inference of deep neural networks. Tensorflow is a symbolic math library based on dataflow and differentiable programming. | |
C3344 | For each value x, multiply the square of its deviation by its probability. (Each deviation has the format x – μ). The mean, μ, of a discrete probability function is the expected value. The standard deviation, Σ, of the PDF is the square root of the variance. | |
C3345 | Cluster analysis, or clustering, is an unsupervised machine learning task. It involves automatically discovering natural grouping in data. Unlike supervised learning (like predictive modeling), clustering algorithms only interpret the input data and find natural groups or clusters in feature space. | |
C3346 | Just as ordinary least square regression is the method used to estimate coefficients for the best fit line in linear regression, logistic regression uses maximum likelihood estimation (MLE) to obtain the model coefficients that relate predictors to the target. | |
C3347 | Bayesian decision theory is a fundamental statistical approach to the problem of pattern classification. This approach is based on quantifying the tradeoffs between various classification decisions using probability and the costs that accompany such decisions. | |
C3348 | The steps in grouping may be summarized as follows:Decide on the number of classes.Determine the range, i.e., the difference between the highest and lowest observations in the data.Divide range by the number of classes to estimate approximate size of the interval (h).More items | |
C3349 | You CAN use linear regression with ordinal data, because you can regress any set of numbers against any other. The problems come when interpreting the results. You CAN use linear regression with ordinal data, because you can regress any set of numbers against any other. | |
C3350 | For example for a t-test, we assume that a random variable follows a normal distribution. For discrete data key distributions are: Bernoulli, Binomial, Poisson and Multinomial. | |
C3351 | Rank one matrices The rank of a matrix is the dimension of its column (or row) space. The matrix. 1 4 5 A = 2 8 10 2 Page 3 � � has rank 1 because each of its columns is a multiple of the first column. | |
C3352 | A scatterplot is a type of data display that shows the relationship between two numerical variables. Each member of the dataset gets plotted as a point whose x-y coordinates relates to its values for the two variables. | |
C3353 | Preventing the error gradients from vanishing The presence of the forget gate's activations allows the LSTM to decide, at each time step, that certain information should not be forgotten and to update the model's parameters accordingly. and the gradient doesn't vanish. | |
C3354 | Step 1: Learn the fundamental data structures and algorithms. First, pick a favorite language to focus on and stick with it. Step 2: Learn advanced concepts, data structures, and algorithms. Step 1+2: Practice. Step 3: Lots of reading + writing. Step 4: Contribute to open-source projects. Step 5: Take a break. | |
C3355 | In physics, mathematics and statistics, scale invariance is a feature of objects or laws that do not change if scales of length, energy, or other variables, are multiplied by a common factor, and thus represent a universality. | |
C3356 | Bayesian classification is based on Bayes' Theorem. Bayesian classifiers are the statistical classifiers. Bayesian classifiers can predict class membership probabilities such as the probability that a given tuple belongs to a particular class. | |
C3357 | Sample variance Dividing instead by n − 1 yields an unbiased estimator. In other words, the expected value of the uncorrected sample variance does not equal the population variance σ2, unless multiplied by a normalization factor. The sample mean, on the other hand, is an unbiased estimator of the population mean μ. | |
C3358 | Types of InferencePoint Estimation.Interval Estimation.Hypothesis Testing. | |
C3359 | Contrastive Loss: Contrastive refers to the fact that these losses are computed contrasting two or more data points representations. This name is often used for Pairwise Ranking Loss, but I've never seen using it in a setup with triplets. Triplet Loss: Often used as loss name when triplet training pairs are employed. | |
C3360 | “Statistical significance helps quantify whether a result is likely due to chance or to some factor of interest,” says Redman. When a finding is significant, it simply means you can feel confident that's it real, not that you just got lucky (or unlucky) in choosing the sample. | |
C3361 | Definition. Univariate analyses are used extensively in quality of life research. Univariate analysis is defined as analysis carried out on only one (“uni”) variable (“variate”) to summarize or describe the variable (Babbie, 2007; Trochim, 2006). | |
C3362 | Reinforcement Learning WorkflowCreate the Environment. First you need to define the environment within which the agent operates, including the interface between agent and environment. Define the Reward. Create the Agent. Train and Validate the Agent. Deploy the Policy. | |
C3363 | 3 layers | |
C3364 | The derivative of the sigmoid is ddxσ(x)=σ(x)(1−σ(x)). | |
C3365 | The consequent of a conditional statement is the part that usually follows "then". The part that usually follows "if" is called the "antecedent". To affirm the consequent is, of course, to claim that the consequent is true. Thus, affirming the consequent in the example would be to claim that I have logic class. | |
C3366 | In machine learning, the delta rule is a gradient descent learning rule for updating the weights of the inputs to artificial neurons in a single-layer neural network. It is a special case of the more general backpropagation algorithm. | |
C3367 | 10 things to consider before choosing enterprise analytics platformAnalytic approach and data accuracy.Features and Tracking Types.Connectivity and Integration.Professional Services and Support.Data Storage Options: SaaS vs. Self-Hosting.Legal compliance.Supplier and Software Reliability.Ongoing and future costs.More items• | |
C3368 | ⏩ optimal policy: the best action to take at each state, for maximum rewards over time. To help our agent do this, we need two things: A way to determine the value of a state in MDP. An estimated value of an action taken at a particular state. | |
C3369 | Non-hierarchical cluster analysis aims to find a grouping of objects which maximises or minimises some evaluating criterion. Many of these algorithms will iteratively assign objects to different groups while searching for some optimal value of the criterion. | |
C3370 | Area Under Curve(AUC) is one of the most widely used metrics for evaluation. It is used for binary classification problem. AUC of a classifier is equal to the probability that the classifier will rank a randomly chosen positive example higher than a randomly chosen negative example. | |
C3371 | Adding Noise into Neural Network Neural networks are capable of learning output functions that can change wildly with small changes in input. Adding noise to inputs randomly is like telling the network to not change the output in a ball around your exact input. | |
C3372 | In natural language processing, word sense disambiguation (WSD) is the problem of determining which "sense" (meaning) of a word is activated by the use of the word in a particular context, a process which appears to be largely unconscious in people. | |
C3373 | Multicollinearity is a problem because it undermines the statistical significance of an independent variable. Other things being equal, the larger the standard error of a regression coefficient, the less likely it is that this coefficient will be statistically significant. | |
C3374 | Strong AI has a complex algorithm that helps it act in different situations, while all the actions in weak AIs are pre-programmed by a human. Strong AI-powered machines have a mind of their own. They can process and make independent decisions, while weak AI-based machines can only simulate human behavior. | |
C3375 | In Electrical Engineering, Calculus (Integration) is used to determine the exact length of power cable needed to connect two substations, which are miles away from each other. Space flight engineers frequently use calculus when planning for long missions. | |
C3376 | A hidden Markov model (HMM) is an augmentation of the Markov chain to include observations. Just like the state transition of the Markov chain, an HMM also includes observations of the state. The observations are modeled using the variable Ot for each time t whose domain is the set of possible observations. | |
C3377 | A compound proposition that is always True is called a tautology. Two propositions p and q are logically equivalent if their truth tables are the same. Namely, p and q are logically equivalent if p ↔ q is a tautology. If p and q are logically equivalent, we write p ≡ q. | |
C3378 | Backward chaining (or backward reasoning) is an inference method described colloquially as working backward from the goal. It is used in automated theorem provers, inference engines, proof assistants, and other artificial intelligence applications. Both rules are based on the modus ponens inference rule. | |
C3379 | Basically, predicting a continuous variable is termed as regression. There are a no of regression algorithms like ridge and lasso regression you may want to check out.Linear Regression.Logistic Regression.Polynomial Regression.Stepwise Regression.Ridge Regression.Lasso Regression.ElasticNet Regression, | |
C3380 | A regression model will have unit changes between the x and y variables, where a single unit change in x will coincide with a constant change in y. Taking the log of one or both variables will effectively change the case from a unit change to a percent change. | |
C3381 | When we do further analysis, like multivariate linear regression, for example, the attributed income will intrinsically influence the result more due to its larger value. But this doesn't necessarily mean it is more important as a predictor. So we normalize the data to bring all the variables to the same range. | |
C3382 | The term convolution refers to the mathematical combination of two functions to produce a third function. It merges two sets of information. In the case of a CNN, the convolution is performed on the input data with the use of a filter or kernel (these terms are used interchangeably) to then produce a feature map. | |
C3383 | It results in a biased sample, a non-random sample of a population (or non-human factors) in which all individuals, or instances, were not equally likely to have been selected. If this is not accounted for, results can be erroneously attributed to the phenomenon under study rather than to the method of sampling. | |
C3384 | Parametric tests assume underlying statistical distributions in the data. Nonparametric tests do not rely on any distribution. They can thus be applied even if parametric conditions of validity are not met. | |
C3385 | In a deep learning architecture, the output of each intermediate layer can be viewed as a representation of the original input data. The input at the bottom layer is raw data, and the output of the final layer is the final low-dimensional feature or representation. | |
C3386 | Solutions to this are to decrease your network size, or to increase dropout. For example you could try dropout of 0.5 and so on. If your training/validation loss are about equal then your model is underfitting. Increase the size of your model (either number of layers or the raw number of neurons per layer) | |
C3387 | Standardizing Neural Network Data. In theory, it's not necessary to normalize numeric x-data (also called independent data). However, practice has shown that when numeric x-data values are normalized, neural network training is often more efficient, which leads to a better predictor. | |
C3388 | Types of Activation FunctionsSigmoid Function. In an ANN, the sigmoid function is a non-linear AF used primarily in feedforward neural networks. Hyperbolic Tangent Function (Tanh) Softmax Function. Softsign Function. Rectified Linear Unit (ReLU) Function. Exponential Linear Units (ELUs) Function. | |
C3389 | Use Fisher's exact test when you have two nominal variables. Fisher's exact test will tell you whether this difference between 81 and 31% is statistically significant. A data set like this is often called an "R×C table," where R is the number of rows and C is the number of columns. | |
C3390 | The word "deep" in "deep learning" refers to the number of layers through which the data is transformed. Deep models (CAP > 2) are able to extract better features than shallow models and hence, extra layers help in learning the features effectively. | |
C3391 | There is a layer of input nodes, a layer of output nodes, and one or more intermediate layers. The interior layers are sometimes called “hidden layers” because they are not directly observable from the systems inputs and outputs. | |
C3392 | Moments are a set of statistical parameters to measure a distribution. Four moments are commonly used: 1st, Mean: the average. | |
C3393 | We define the cross-entropy cost function for this neuron by C=−1n∑x[ylna+(1−y)ln(1−a)], where n is the total number of items of training data, the sum is over all training inputs, x, and y is the corresponding desired output. It's not obvious that the expression (57) fixes the learning slowdown problem. | |
C3394 | AdaBoost algorithm, short for Adaptive Boosting, is a Boosting technique that is used as an Ensemble Method in Machine Learning. It is called Adaptive Boosting as the weights are re-assigned to each instance, with higher weights to incorrectly classified instances. | |
C3395 | Regression toward the mean occurs for two reasons. First, it results because you asymmetrically sampled from the population. If you randomly sample from the population, you would observe (subject to random error) that the population and your sample have the same pretest average. | |
C3396 | Face validity is only considered to be a superficial measure of validity, unlike construct validity and content validity because is not really about what the measurement procedure actually measures, but what it appears to measure. This appearance is only superficial. | |
C3397 | Here are some important considerations while choosing an algorithm.Size of the training data. It is usually recommended to gather a good amount of data to get reliable predictions. Accuracy and/or Interpretability of the output. Speed or Training time. Linearity. Number of features. | |
C3398 | Based on a rule of thumb, it can be said that RMSE values between 0.2 and 0.5 shows that the model can relatively predict the data accurately. In addition, Adjusted R-squared more than 0.75 is a very good value for showing the accuracy. In some cases, Adjusted R-squared of 0.4 or more is acceptable as well. | |
C3399 | Generally, the data is arranged from smallest to largest: First quartile: the lowest 25% of numbers. Second quartile: between 25.1% and 50% (up to the median) Third quartile: 51% to 75% (above the median) |
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