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C6600 | Machine Learning AlgorithmsLinear Regression. To understand the working functionality of this algorithm, imagine how you would arrange random logs of wood in increasing order of their weight. Logistic Regression. Decision Tree. SVM (Support Vector Machine) Naive Bayes. KNN (K- Nearest Neighbors) K-Means. Random Forest.More items• | |
C6601 | As opposed to decision tree and rule set induction, which result in classification models, association rule learning is an unsupervised learning method, with no class labels assigned to the examples. This would then be a Supervised Learning task , where the NN learns from pre-calssified examples. | |
C6602 | 8 Methods to Boost the Accuracy of a ModelAdd more data. Having more data is always a good idea. Treat missing and Outlier values. Feature Engineering. Feature Selection. Multiple algorithms. Algorithm Tuning. Ensemble methods. | |
C6603 | In the context of CNN, a filter is a set of learnable weights which are learned using the backpropagation algorithm. You can think of each filter as storing a single template/pattern. Filter is referred to as a set of shared weights on the input. | |
C6604 | Prior probability, in Bayesian statistical inference, is the probability of an event before new data is collected. This is the best rational assessment of the probability of an outcome based on the current knowledge before an experiment is performed. | |
C6605 | The Kappa Architecture was first described by Jay Kreps. It focuses on only processing data as a stream. It is not a replacement for the Lambda Architecture, except for where your use case fits. The idea is to handle both real-time data processing and continuous reprocessing in a single stream processing engine. | |
C6606 | An SVM possesses a number of parameters that increase linearly with the linear increase in the size of the input. A NN, on the other hand, doesn't. Even though here we focused especially on single-layer networks, a neural network can have as many layers as we want. | |
C6607 | An independent event is an event in which the outcome isn't affected by another event. A dependent event is affected by the outcome of a second event. | |
C6608 | While Neural Networks use neurons to transmit data in the form of input values and output values through connections, Deep Learning is associated with the transformation and extraction of feature which attempts to establish a relationship between stimuli and associated neural responses present in the brain. | |
C6609 | An example is the weight of luggage loaded onto an airplane. Counting the number of times a ball dropped from a rooftop bounces before it comes to rest comprises numerical data.On the other hand, non-numerical data, also called categorical, qualitative or Yes/No data, is data that can be observed, not measured. | |
C6610 | In statistics, scale analysis is a set of methods to analyze survey data, in which responses to questions are combined to measure a latent variable. Any measurement for such data is required to be reliable, valid, and homogeneous with comparable results over different studies. | |
C6611 | Random Forest is perhaps the most popular classification algorithm, capable of both classification and regression. It can accurately classify large volumes of data. The name “Random Forest” is derived from the fact that the algorithm is a combination of decision trees. | |
C6612 | Clustering analysis is broadly used in many applications such as market research, pattern recognition, data analysis, and image processing. Clustering can also help marketers discover distinct groups in their customer base. And they can characterize their customer groups based on the purchasing patterns. | |
C6613 | Network StructureGated Recurrent Unit. GRU (Cho14) alternative memory cell design to LSTM. Layer normalization. Adding layer normalization (Ba16) to all linear mappings of the recurrent network speeds up learning and often improves final performance. Feed-forward layers first. Stacked recurrent networks. | |
C6614 | A common strategy is to grow the tree until each node contains a small number of instances then use pruning to remove nodes that do not provide additional information. Pruning should reduce the size of a learning tree without reducing predictive accuracy as measured by a cross-validation set. | |
C6615 | The method of analyzing an image that has undergone binarization processing is called "blob analysis". A blob refers to a lump. Blob analysis is image processing's most basic method for analyzing the shape features of an object, such as the presence, number, area, position, length, and direction of lumps. | |
C6616 | Rank-reduced singular value decomposition T is a computed m by r matrix of term vectors where r is the rank of A—a measure of its unique dimensions ≤ min(m,n). S is a computed r by r diagonal matrix of decreasing singular values, and D is a computed n by r matrix of document vectors. | |
C6617 | A marginal distribution is the percentages out of totals, and conditional distribution is the percentages out of some column. | |
C6618 | These are the steps we are going to do:Make a stupid model as an example, train and store it.Fetch the variables you need from your stored model.Build the tensor info from them.Create the model signature.Create and save a model builder.Download a Docker image with TensorFlow serving already compile on it.More items• | |
C6619 | 0:382:54Suggested clip · 98 secondsClass Boundaries - YouTubeYouTubeStart of suggested clipEnd of suggested clip | |
C6620 | There is no correct value for MSE. Simply put, the lower the value the better and 0 means the model is perfect. | |
C6621 | In the strictest sense, a nocebo response is where a drug-trial's subject's symptoms are worsened by the administration of an inert, sham, or dummy (simulator) treatment, called a placebo. | |
C6622 | A null hypothesis is a type of hypothesis used in statistics that proposes that there is no difference between certain characteristics of a population (or data-generating process). | |
C6623 | A data distribution is a function or a listing which shows all the possible values (or intervals) of the data. It also (and this is important) tells you how often each value occurs. | |
C6624 | A partition of a number is any combination of integers that adds up to that number. For example, 4 = 3+1 = 2+2 = 2+1+1 = 1+1+1+1, so the partition number of 4 is 5. It sounds simple, yet the partition number of 10 is 42, while 100 has more than 190 million partitions. | |
C6625 | The Central Limit Theorem states that the sampling distribution of the sample means approaches a normal distribution as the sample size gets larger — no matter what the shape of the population distribution. | |
C6626 | Tableau is considered more user-friendly because of its easy drag-and-drop capabilities. QlikView gives better performance because of its patented “Associative Technology” which allows for in-memory processing of the table and at the same time circumvents the use of OLAP Cubing. | |
C6627 | Yes. The reason n-1 is used is because that is the number of degrees of freedom in the sample. The sum of each value in a sample minus the mean must equal 0, so if you know what all the values except one are, you can calculate the value of the final one. | |
C6628 | The z-score statistic converts a non-standard normal distribution into a standard normal distribution allowing us to use Table A-2 in your textbook and report associated probabilities. This discussion combines means, standard deviation, z-score, and probability. | |
C6629 | Augustin Louis Cauchy | |
C6630 | Modus Ponens: "If A is true, then B is true. A is true. Therefore, B is true." Modus Tollens: "If A is true, then B is true. | |
C6631 | Given a character sequence and a defined document unit, tokenization is the task of chopping it up into pieces, called tokens , perhaps at the same time throwing away certain characters, such as punctuation. | |
C6632 | Concepts in Machine Learning can be thought of as a boolean-valued function defined over a large set of training data. We have some attributes/features of the day like, Sky, Air Temperature, Humidity, Wind, Water, Forecast and based on this we have a target Concept named EnjoySport. | |
C6633 | Stratification of clinical trials is the partitioning of subjects and results by a factor other than the treatment given. Stratification can be used to ensure equal allocation of subgroups of participants to each experimental condition. This may be done by gender, age, or other demographic factors. | |
C6634 | Lemmatization is the process of grouping together the different inflected forms of a word so they can be analysed as a single item. Lemmatization is similar to stemming but it brings context to the words. So it links words with similar meaning to one word. | |
C6635 | Contextual bandit is a machine learning framework designed to tackle these—and other—complex situations. With contextual bandit, a learning algorithm can test out different actions and automatically learn which one has the most rewarding outcome for a given situation. | |
C6636 | A test of a statistical hypothesis , where the region of rejection is on only one side of the sampling distribution , is called a one-tailed test. For example, suppose the null hypothesis states that the mean is less than or equal to 10. The alternative hypothesis would be that the mean is greater than 10. | |
C6637 | Unfortunately, causality cannot be established by this observational study, and other work must be done to confirm a cause-and-effect relationship between accumulative deep hypnotic time as measured by Bispectral Index <45 and 1-yr postoperative mortality. | |
C6638 | Top 8 Text Mining ToolsMonkeyLearn | User-friendly text mining.Aylien | Simple API for text mining.IBM Watson | Powerful AI platform.Thematic | Text mining for customer feedback.Google Cloud NLP | Custom machine learning models.Amazon Comprehend | Pre-trained text mining models.More items• | |
C6639 | You still use it, the model in terms of Deep Learning. Beyond this, there is a inherent convolutional depth and complexity inherent to the model in of itself which lends itself to training and otherwise. | |
C6640 | When observed outcome of dependent variable can have multiple possible types then logistic regression will be multinomial. | |
C6641 | The cost parameter decides how much an SVM should be allowed to “bend” with the data. For a low cost, you aim for a smooth decision surface and for a higher cost, you aim to classify more points correctly. It is also simply referred to as the cost of misclassification. | |
C6642 | A model is a simplified representation of a system. over some time period or spatial extent intended to promote understanding of the real system. Why Build a Model? Building models helps us understand the problem. (and its surrounding system) we are investigating solutions for. | |
C6643 | The indicator function 1[0,∞) is right differentiable at every real a, but discontinuous at zero (note that this indicator function is not left differentiable at zero). | |
C6644 | Decision Trees in Machine Learning. Decision Tree models are created using 2 steps: Induction and Pruning. Induction is where we actually build the tree i.e set all of the hierarchical decision boundaries based on our data. Because of the nature of training decision trees they can be prone to major overfitting. | |
C6645 | there are mainly five types of class interval such as exclusive class interval, inclusive class interval, less than class interval, more than class interval, mid value class interval , which has been discussed. | |
C6646 | Back-propagation is the essence of neural net training. It is the practice of fine-tuning the weights of a neural net based on the error rate (i.e. loss) obtained in the previous epoch (i.e. iteration). | |
C6647 | Major advantages include its simplicity and lack of bias. Among the disadvantages are difficulty gaining access to a list of a larger population, time, costs, and that bias can still occur under certain circumstances. | |
C6648 | 2. Stochastic Variational Inference. We derive stochastic variational inference, a stochastic optimization algorithm for mean-field vari- ational inference. Our algorithm approximates the posterior distribution of a probabilistic model with hidden variables, and can handle massive data sets of observations. | |
C6649 | Homogeneity of variance is an assumption underlying both t tests and F tests (analyses of variance, ANOVAs) in which the population variances (i.e., the distribution, or “spread,” of scores around the mean) of two or more samples are considered equal. | |
C6650 | Positive feedback occurs to increase the change or output: the result of a reaction is amplified to make it occur more quickly. Some examples of positive feedback are contractions in child birth and the ripening of fruit; negative feedback examples include the regulation of blood glucose levels and osmoregulation. | |
C6651 | The generator is a convolutional neural network and the discriminator is a deconvolutional neural network. The goal of the generator is to artificially manufacture outputs that could easily be mistaken for real data. The goal of the discriminator is to identify which outputs it receives have been artificially created. | |
C6652 | The probability within the region must not exceed 1. A large number---much larger than 1---multiplied by a small number (the size of the region) can be less than 1 if the latter number is small enough. | |
C6653 | The eigenvalues and eigenvectors of a matrix are often used in the analysis of financial data and are integral in extracting useful information from the raw data. They can be used for predicting stock prices and analyzing correlations between various stocks, corresponding to different companies. | |
C6654 | Most recent answer The number of hidden neurons should be between the size of the input layer and the size of the output layer. The number of hidden neurons should be 2/3 the size of the input layer, plus the size of the output layer. The number of hidden neurons should be less than twice the size of the input layer. | |
C6655 | The Poisson parameter Lambda (λ) is the total number of events (k) divided by the number of units (n) in the data (λ = k/n). In between, or when events are infrequent, the Poisson distribution is used. | |
C6656 | Linear mixed models (sometimes called “multilevel models” or “hierarchical models”, depending on the context) are a type of regression model that take into account both (1) variation that is explained by the independent variables of interest (like lm() ) – fixed effects, and (2) variation that is not explained by the | |
C6657 | Yes a perceptron (one fully connected unit) can be used for regression. It will just be a linear regressor. If you use no activation function you get a regressor and if you put a sigmoid activation you get a classifier. That's why the loss function for classification is called "logistic regression". | |
C6658 | Linear regression is used for predicting the continuous dependent variable using a given set of independent features whereas Logistic Regression is used to predict the categorical. Linear regression is used to solve regression problems whereas logistic regression is used to solve classification problems. | |
C6659 | Yes. This is the architechture of logistic regression, which is similar to a single layer feed forward neural network. | |
C6660 | Binary, multi-class and multi-label classification Cross-entropy is a commonly used loss function for classification tasks. | |
C6661 | Depending on the context, an independent variable is sometimes called a "predictor variable", regressor, covariate, "manipulated variable", "explanatory variable", exposure variable (see reliability theory), "risk factor" (see medical statistics), "feature" (in machine learning and pattern recognition) or "input | |
C6662 | For example, if you have daily sales data and you expect that it exhibits annual seasonality, you should have more than 365 data points to train a successful model. If you have hourly data and you expect your data exhibits weekly seasonality, you should have more than 7*24 = 168 observations to train a model. | |
C6663 | The availability bias happens we people often judge the likelihood of an event, or frequency of its occurrence by the ease with which examples and instances come easily to mind. Most consumers are poor at risk assessments – for example they over-estimate the likelihood of attacks by sharks or list accidents. | |
C6664 | Typically, a one-way ANOVA is used when you have three or more categorical, independent groups, but it can be used for just two groups (but an independent-samples t-test is more commonly used for two groups). | |
C6665 | Linear transformation is a function between two linear spaces over the same field of scalars, which is additive and homogeneous. Linear operator is a linear transformation for which the domain and the codomain spaces are the same and, moreover, in both of them the same basis is considered. | |
C6666 | Typically, a sample survey consists of the following steps:Define the target population. Select the sampling scheme and sample size. Develop the questionnaire. Recruit and train the field investigators. Obtain information as per the questionnaire. Scrutinize the information gathered. Analyze and interpret the information. | |
C6667 | Task parallelism is the simultaneous execution on multiple cores of many different functions across the same or different datasets. Data parallelism (aka SIMD) is the simultaneous execution on multiple cores of the same function across the elements of a dataset. | |
C6668 | The ReLU function is another non-linear activation function that has gained popularity in the deep learning domain. ReLU stands for Rectified Linear Unit. The main advantage of using the ReLU function over other activation functions is that it does not activate all the neurons at the same time. | |
C6669 | The Purpose of Statistics: Statistics teaches people to use a limited sample to make intelligent and accurate conclusions about a greater population. The use of tables, graphs, and charts play a vital role in presenting the data being used to draw these conclusions. | |
C6670 | The standard score (more commonly referred to as a z-score) is a very useful statistic because it (a) allows us to calculate the probability of a score occurring within our normal distribution and (b) enables us to compare two scores that are from different normal distributions. | |
C6671 | A traditional default value for the learning rate is 0.1 or 0.01, and this may represent a good starting point on your problem. — Practical recommendations for gradient-based training of deep architectures, 2012. | |
C6672 | In logistic regression, as with any flavour of regression, it is fine, indeed usually better, to have continuous predictors. Given a choice between a continuous variable as a predictor and categorising a continuous variable for predictors, the first is usually to be preferred. | |
C6673 | Use caution unless you have good reason and data to support using the substitute value. Regression Substitution: You can use multiple-regression analysis to estimate a missing value. We use this technique to deal with missing SUS scores. Regression substitution predicts the missing value from the other values. | |
C6674 | Usually in a conventional neural network, one tries to predict a target vector y from input vectors x. In an autoencoder network, one tries to predict x from x. Sometimes it is and the neural network simply learns to duplicate the training data instead of learning general concepts from the training data. | |
C6675 | Most machine learning algorithms operate based on the assumption that there are many more samples than predictors. The number of samples (n) are the actual samples drawn from the domain that you must use to model your predictive modeling problem. | |
C6676 | Pseudorandomness measures the extent to which a sequence of numbers, "though produced by a completely deterministic and repeatable process, appear to be patternless." The pattern's seeming randomness is "the crux of" much online and other security. | |
C6677 | An S-curve is simply a curve of some object, line or path in the image that curves back and forth horizontally as you proceed vertically, much like the letter S–in fact, usually exactly like the letter S. | |
C6678 | Say we want to estimate the mean of a population. While the most used estimator is the average of the sample, another possible estimator is simply the first number drawn from the sample. In theory, you could have an unbiased estimator whose variance is asymptotically nonzero, and that would be inconsistent. | |
C6679 | One more difference is that Pearson works with raw data values of the variables whereas Spearman works with rank-ordered variables. Now, if we feel that a scatterplot is visually indicating a “might be monotonic, might be linear” relationship, our best bet would be to apply Spearman and not Pearson. | |
C6680 | Active learning is generally defined as any instructional method that engages students in the learning process. In short, active learning requires students to do meaningful learning activities and think about what they are doing. The students work individually on assignments, and cooperation is limited. | |
C6681 | In mathematics and statistics, a stationary process (or a strict/strictly stationary process or strong/strongly stationary process) is a stochastic process whose unconditional joint probability distribution does not change when shifted in time. For many applications strict-sense stationarity is too restrictive. | |
C6682 | Deep learning architectures such as deep neural networks, deep belief networks, recurrent neural networks and convolutional neural networks have been applied to fields including computer vision, machine vision, speech recognition, natural language processing, audio recognition, social network filtering, machine | |
C6683 | The F Distribution The distribution of all possible values of the f statistic is called an F distribution, with v1 = n1 - 1 and v2 = n2 - 1 degrees of freedom. The mean of the distribution is equal to v2 / ( v2 - 2 ) for v2 > 2. | |
C6684 | The predictive power of a scientific theory refers to its ability to generate testable predictions. Theories with strong predictive power are highly valued, because the predictions can often encourage the falsification of the theory. | |
C6685 | The agglomerative clustering is the most common type of hierarchical clustering used to group objects in clusters based on their similarity. It's also known as AGNES (Agglomerative Nesting). The algorithm starts by treating each object as a singleton cluster. | |
C6686 | Methods of Data Labeling in Machine LearningReinforcement Learning. The method utilizes the trial-and-error approach to make predictions within a specific context using feedback from their own experience. Supervised Learning. This method requires a huge amount of manually labeled data. Unsupervised Learning. The method leverages raw or unstructured data. | |
C6687 | Summary: “OLS” stands for “ordinary least squares” while “MLE” stands for “maximum likelihood estimation.” Maximum likelihood estimation, or MLE, is a method used in estimating the parameters of a statistical model and for fitting a statistical model to data. | |
C6688 | Writing up resultsFirst, present descriptive statistics in a table. Organize your results in a table (see Table 3) stating your dependent variable (dependent variable = YES) and state that these are "logistic regression results." When describing the statistics in the tables, point out the highlights for the reader.More items | |
C6689 | Examples of Deep Learning at Work Aerospace and Defense: Deep learning is used to identify objects from satellites that locate areas of interest, and identify safe or unsafe zones for troops. Medical Research: Cancer researchers are using deep learning to automatically detect cancer cells. | |
C6690 | Variational autoencoders (VAEs) are a deep learning technique for learning latent representations. They have also been used to draw images, achieve state-of-the-art results in semi-supervised learning, as well as interpolate between sentences. There are many online tutorials on VAEs. | |
C6691 | A multiplicative error model is one in which the dependent variable is a product of the independent variable and an error term, instead of a sum. | |
C6692 | "The difference between discrete choice models and conjoint models is that discrete choice models present experimental replications of the market with the focus on making accurate predictions regarding the market, while conjoint models do not, using product profiles to estimate underlying utilities (or partworths) | |
C6693 | The normal distribution is the most important probability distribution in statistics because it fits many natural phenomena. For example, heights, blood pressure, measurement error, and IQ scores follow the normal distribution. It is also known as the Gaussian distribution and the bell curve. | |
C6694 | So, a covariate is in fact, a type of control variable. Examples of a covariate may be the temperature in a room on a given day of an experiment or the BMI of an individual at the beginning of a weight loss program. Covariates are continuous variables and measured at a ratio or interval level. | |
C6695 | Word2vec is a technique for natural language processing. The word2vec algorithm uses a neural network model to learn word associations from a large corpus of text. Once trained, such a model can detect synonymous words or suggest additional words for a partial sentence. | |
C6696 | A restricted Boltzmann machine (RBM) is a generative stochastic artificial neural network that can learn a probability distribution over its set of inputs. Restricted Boltzmann machines can also be used in deep learning networks. | |
C6697 | Item response Theory(IRT) is a way to analyze responses to tests or questionnaires with the goal of improving measurement accuracy and reliability. | |
C6698 | Generative model is a class of models for Unsupervised learning where given training data our goal is to try and generate new samples from the same distribution. To train a Generative model we first collect a large amount of data in some domain (e.g., think millions of images, sentences, or sounds, etc.) | |
C6699 | Forward chaining as the name suggests, start from the known facts and move forward by applying inference rules to extract more data, and it continues until it reaches to the goal, whereas backward chaining starts from the goal, move backward by using inference rules to determine the facts that satisfy the goal. |
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