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15,200 | proofthe cdf of is given by [ zp[( )/ zp[ szz +sz - dx - / dy ph( ) where we make change of variable ( )/ in the fourth equation hencez ( the rescaling procedure in theorem is called standardization it follows from theorem that any (us can be written as szstandardization where ( in other wordsany normal random variable... |
15,201 | which is an affine combination of independent standard normal random vectors hencey is multivariate normal its expectation vector and covariance matrix can be found easily from theorem the next theorem shows that the distribution of subvector of multivariate normal random vector is again normal theorem marginal distrib... |
15,202 | - - - ( is completed by observing that > - cr ( cr and - sq > - sr cq > sr cr cr cq cq cr |{zb cr if and xq are independentthen they are obviously uncorrelatedas sr [( )(xq uq ) ( (xq uq ) converselyif sr othen by ( the conditional distribution of xq given is the same as the unconditional distribution of xq that isn(uq... |
15,203 | multivariate normal distribution theorem relationship between normal and noncentral kh distributions let (uin be an -dimensional normal random vector and let vk vm be linear subspaces of dimensions and mrespectivelywith let xk and xm be orthogonal projections of onto vk and vm and let uk and um be the corresponding pro... |
15,204 | distributionswe have wc- (wv) - -wv/ vd- - / dv vc+ - -( + ) / dv fw (wc + ( ( (cg( ( dwc- (cg( ( ) + where the last equality follows from the fact that the integrand is equal to ( ) - times the density of the gamma(aldistribution with and ( )/ the density of mn uv is given by fz (zfw ( /nm/ the proof is completed by c... |
15,205 | convergence in probability refers only to the distribution of xn insteadif the sequence is defined on common probability spacethen we can consider the following mode of convergence that uses the joint distribution of the sequence of random variables definition almost sure convergence the sequence of random variables co... |
15,206 | the generalization to random vectors replaces ( with lim [xn ap[ afor all rn such that [ ( where denotes the boundary of the set useful tool for demonstrating convergence in distribution is the characteristic function ps of random vector xdefined as the expectationps ( : eit rn ( the moment generating function in ( is ... |
15,207 | as example (convergence in distributiondefine the random variables ! xk yn : = iid where ber( / we now show that yn - ( firstnote that exp(ityn ( exp(it/ ) exp(itxk / - = = secondfrom the collapsing product( exp(it/ )nk= ( exp(it/ ) exp(it)we have / exp(ityn ( exp(it) exp(it/ it follows that limne exp(ityn (exp(it )/(i... |
15,208 | definition complete convergence the sequence of random variables is said to converge completely to if for all [|xn xen cpl we denote the complete convergence as xn - complete convergence example (complete and almost sure convergencewe show that complete convergence implies almost sure convergence we can bound the crite... |
15,209 | proof firstwe show that xn - xn - using the inequality [ [afor any event to this endconsider the distribution function of xf xn (xp[xn xp[xn |xn xep[xn |xn [|xn xep[xn xx xn [|xn xep[ enowin the arguments above we can switch the roles of xn and (there is symmetryto deduce the analogous resultf ( [| xn ep[xn ethereforem... |
15,210 | proofwe prove the theorem for xand scalar the proof for random vectors is analogxn ous firstwe show that :-= usingfor exampletheorem in yn other wordswe wish to show that the characteristic function of the joint distribution of xn and yn converges pointwise as psxn ,yn (te ei( xn + yn -eit eit psx, ( ) to show the limi... |
15,211 | that by definition the average converges to in -norm as that isx - in factto obtain convergence in probability the variance need not be finite -it is sufficient to assume that ex theorem weak law of large numbers law of large numbers if xn are iid with finite expectation uthen for all lim | ue np in other wordsx - theh... |
15,212 | prooffirstnote that any random variable can be written as the difference of two nonnegative random variablesx xxwhere :max{ and :min{ thuswithout loss of generalitywe assume that the random variables in the theorem above are nonnegative secondfrom the sequence { we can pick up the subsequence { ={ thenfrom chebyshev' i... |
15,213 | an application of taylor' theorem in the neighborhood of yields psz (tn ps ( ps ( ( / since ps ( dtd eitz = ez and ps ( ez - we have# in psn (tpsz (tn ( / - - / from example we recognize - / as the characteristic function of the standard normal distribution thusfrom theorem we conclude that - ( figure shows the central... |
15,214 | denoted thn is the solution to the system of equations that results from approximating the expectation with respect to using an average of iid copies of xn psn (th: ps(xi thn = thuspsn ( thn theorem -estimator the -estimator is asymptotically normal as ( thn th- ( - ba-)( where :- ps ( thand : ps( thps( th)is the covar... |
15,215 | laplace' approximation finallywe mention laplace' approximationwhich shows how integrals or expectations behave under the normal distribution with vanishingly small variance theorem laplace' approximation suppose that thn thwhere thlies in the interior of the open set th and that sn is covariance matrix such that sn sl... |
15,216 | where th is point that lies on the line segment joining thand th since this unique global minimumthere must be small enough neighborhood of thsay thsuch that is strictly (also known as stronglyconvex function on th in other wordsh(this positive definite matrix for all th th and there exists smallest positive eigenvalue... |
15,217 | timehomogeneous transition density initial distribution the index in xt is usually seen as "timeor "stepparameter the index set { in the definition above was chosen out of convenience it can be replaced by any countable index set we restrict ourselves to time-homogeneous markov chains -markov chains for which the condi... |
15,218 | stated transition probabilities for the reversed chain in particularfor reversible markov chain we have (xq( xf (yq( yfor all xy ( these are the detailed (or localbalance equations note that the detailed balance equations imply the global balance equations henceif markov chain is irreducible and there exists pdf such t... |
15,219 | (cvalidate the model bayesian statistics bayesian statisticsin this approach we average over all possible values of the parameter th using user-specified weight function (thand obtain the model (thg(thdth for practical computationsthis means that we can treat th as random variable with pdf (thbayesformula (th tg( thg(t... |
15,220 | where and are the times required to calculate the estimators and respectively in this schemet is considered more efficient than if its relative time variance product is smaller we discuss next two systematic approaches for constructing sound estimators method of moments suppose xn are outcomes from an iid sample xn ~ii... |
15,221 | be iid copies of -dimensional random vector with mean vector and covariance matrix we assume the moment estimator for isas in the casex ( xn )/ as the covariance matrix can be written (see ( )as ( )( )the method of moments yields the estimator (xi )(xi )sn = sample covariance matrix similar to the one-dimensional case ... |
15,222 | if (th tis differentiable function with respect to th and the maximum is attained in the interior of thand there exists unique maximum pointthen we can find the mle of th by solving the equations (th thi example (bernoulli random samplesuppose we have data tn { xn and assume the model xn ~iid ber(ththenthe likelihood f... |
15,223 | example (approximate confidence interval for the meanlet xn be an iid sample from distribution with mean and variance (both assumed to be unknownby the central limit theorem and the law of large numberstx approx ( )sn for large nwhere is the sample standard deviation rearranging the approximate equality [| - / awhere -... |
15,224 | decision rule reject if the -value is smaller than some significance level the -value is the probability thatunder the (randomtest statistic takes value as extreme as or more extreme than the one observed in particularif is the observed outcome of the test statistic then -value left one-sided testp :ph [ ]right one-sid... |
15,225 | in other wordsthe -value of the test is the smallest level of significance that would lead to the rejection of in generala statistical test involves the following steps formulate an appropriate statistical model for the data give the null ( and alternative ( hypotheses in terms of the parameters of the model determine ... |
15,226 | further reading accessible treatises on probability and stochastic processes include [ kallenberg' book [ provides complete graduate-level overview of the foundations of modern probability details on the convergence of probability measures and limit theorems can be found in [ for an accessible introduction to mathemati... |
15,227 | ython rimer python has become the programming language of choice for many researchers and practitioners in data science and machine learning this appendix gives brief introduction to the language as the language is under constant development and each year many new packages are being releasedwe do not pretend to be exha... |
15,228 | object python programs the output for these two modes of input can differ slightly for exampletyping variable name in the console causes its contents to be automatically printedwhereas in python program this must be done explicitly by calling the print function selecting (highlightingseveral program lines in spyder and... |
15,229 | [ [ : elements with index from to [: all elements with index less than [ :all elements with index or more - :the last two elements [ [ [ [ an operator is programming language construct that performs an action on one or more operands the action of an operator in python depends on the type of the operand(sfor exampleoper... |
15,230 | class (see section can be thought of as template for creating custom type of object hello dir(sprint (dflush =trueprint the list in flushed format ['__add__ ''__class__ ''__contains__ ''__delattr__ ''__dir__ '(many left out'replace ''rfind ''rindex ''rjust ''rpartition ''rsplit ''rstrip ''split ''splitlines ''startswit... |
15,231 | the assignment operator=assigns an object to variablee an expression is combination of valuesoperatorsand variables that yields another value or variable assignment variable names are case sensitive and can only contain lettersnumbersand underscores they must start with either letter or underscore note that reserved wo... |
15,232 | several of the numerical operators can be combined with an assignment operatoras in + to mean operators such as and can be defined for other data types as wellwhere they take on different meaning this is called operator overloadingan example of which is the use of for list repetition as we saw earlier function function... |
15,233 | from numpy import array square sqrt array ([ , , ]def stat( ) len( #the length of meanx sum( )/ stdx sqrt(sumsquare ( meanx ))/nreturn [meanx ,stdxprint (stat( )[ basic math functions such as sqrt are unknown to the standard python interpreter and need to be imported more on this in section below as was already mention... |
15,234 | namespace when imported into another python filethe module name is treated as namespaceproviding naming system where each object has its unique name for exampledifferent modules mod and mod can have different sum functionsbut they can be distinguished by prefixing the function name with the module name via the dot nota... |
15,235 | this avoids the tedious prefixing of functions via the (aliasof the module name howeverfor large programs it is good practice to always use the prefix/alias name constructionto be able to clearly ascertain precisely which module function being used belongs to flow control flow control in python is similar to that of ma... |
15,236 | iteration iterating over sequence of objectssuch as used in for loopis common operation to better understand how iteration workswe consider the following code hello for in sprint ( ,'*'end=' iterable iterator string is an example of python object that can be iterated one of the methods of string object is __iter__ any ... |
15,237 | { { for in aprint (iprint ( { useful way to construct lists is by list comprehensionthat isby expressions of the form list comprehension for in if for sets similar construction holds in this waylists and sets can be defined using very similar syntax as in mathematics comparefor examplethe mathematical definition of the... |
15,238 | class def __init__(self)instance the main inbuilt method is __init__which creates an instance of class object for examplestr is class object (string class)but str('hello'or simply 'hello'creates an instancesof the str class instance attributes are created during initialization and their values may be different for diff... |
15,239 | the derived class (initiallyinherits all of the attributes of the parent class as an examplethe class shire_person below inherits the attributes nameageand address from its parent class person this is done using the super functionused here to refer to the parent class person without naming it explicitly when creating n... |
15,240 | introductionbracket syntax {iprovides placeholder for the -th variable to be printedwith being the first index the format for the output is further specified by { :format}where format is typically of the form[width]precision][typein this specificationwidth specifies the minimum width of outputprecision specifies the nu... |
15,241 | when dealing with file input and output it is important to always close files files that remain opene when program finishes unexpectedly due to programming errorcan cause considerable system problems for this reason it is recommended to open files via context management the syntax is as follows with open('output txt ''... |
15,242 | numpy the package numpy (module name numpyprovides the building blocks for scientific computing in python it contains all the standard mathematical functionssuch as sincostanetc as well as efficient functions for random number generationlinear algebraand statistical computation import numpy as np import the package np ... |
15,243 | [[ [ ]the dimension of an ndarray can be obtained via its shape methodwhich returns tuple arrays can be reshaped via the reshape method this does not change the current ndarray object to make the change permanenta new instance needs to be created np array range ( )# is an ndarray of shape ( ,print ( shape reshape ( , #... |
15,244 | [ ]slicing arrays can be sliced similarly to python lists if an array has several dimensionsa slice for each dimension needs to be specified recall that python indexing starts at ' and ends at 'len(obj)- the following program illustrates various slicing operations np array range ( )reshape ( , print (aprint ( [ ]first ... |
15,245 | print (np sqrt( )[[ [ ]in order to compute matrix multiplications and compute inner products of vectorsnumpy' dot function can be usedeither as method of an ndarray instance or as method of np print (np dot( , )[[ [ ]print ( dot( )same as np dot( , [[ [ ]since version of pythonit is possible to multiply two ndarrays us... |
15,246 | [ ][ [ ]note that above is duplicated row-wise and column-wise broadcasting also applies to the matrix-wise operator @as illustrated below herethe matrix is duplicated across the third dimension resulting in the two matrix multiplications ## and np arange ( reshape ( , , np arange ( reshape ( , print ( @ [[ ][[ [ ]]fun... |
15,247 | matplotlib the main python graphics library for and plotting is matplotliband its subpackage pyplot contains collection of functions that make plotting in python similar to that in matlab creating basic plot the code below illustrates various possibilities for creating plots the style and color of lines and markers can... |
15,248 | histscat py import matplotlib pyplot as plt import numpy as np np random randn ( np random randn ( np random randn ( plt subplot ( first subplot plt hist( ,bins = facecolor =' 'plt xlabel (' variable 'plt ylabel ('counts 'plt subplot ( second subplot plt scatter ( , , =' 'alpha = plt show ( counts variable figure histo... |
15,249 | zarray npdf(xarray yarray fig plt figure figsize =plt figaspect ( ax fig add_subplot ( projection =' 'ax scatter ( , ,zc=' 'ax set_xlabel ('$ 'ax set_ylabel ('$ 'ax set_zlabel ('$ ( , )$'ax fig add_subplot ( projection =' 'ax plot_surface (xarray ,yarray ,zarray ,cmap='viridis 'edgecolor ='none 'ax set_xlabel ('$ 'ax s... |
15,250 | for creating series object is series pd series(index=['index']heresome -dimensional data structuresuch as -dimensional ndarraya listor dictionaryand index is list of names of the same length as when is dictionarythe index is created from the keys of the dictionary when is an ndarray and index is omittedthe default inde... |
15,251 | manipulating data frames often data encoded in dataframe or series objects need to be extractedalteredor combined gettingsettingand deleting columns works in similar manner as for dictionaries the following code illustrates various operations ages [ , , , , , , , ='gender ':[' '' ']* 'age 'agesdf pd dataframe (ddf at[ ... |
15,252 | gender object age int dtype object gender category age int dtype object + extracting information extracting statistical information from dataframe object is facilitated by large collection of methods (functionsin pandas table gives selection of data inspection methods see for their practical use the code below provides... |
15,253 | table useful pandas methods for data inspection columns column names count counts number of non-na cells crosstab cross-tabulate two or more categories describe summary statistics dtypes data types for each column head display the top rows of dataframe groupby group data by column(sinfo display information about the da... |
15,254 | print (df groupby (' 'agg ([sum ,np mean ]) sum mean sum mean plotting the plot method of dataframe makes plots of dataframe using matplotlib different types of plot can be accessed via the kind 'strconstructionwhere str is one of line (default)barhistboxkdeand several more finer controlsuch as modifying the fontis obt... |
15,255 | partitioning the data randomly partitioning the data in order to test the model may be achieved easily with sklearn' function train_test_split for examplesuppose that the training data is described by the matrix of explanatory variables and the vector of responses then the following code splits the data set into traini... |
15,256 | figure example training (circlesand test (squaresset for two class classification explanatory variables are the (xycoordinatesclasses are zero (greenor one (bluefrom sklearn import preprocessing min_max_scaler preprocessing minmaxscaler feature_range =( )x_scaled min_max_scaler fit_transform (xequivalent tox_scaled ( m... |
15,257 | test data the following code does this for the data shown in figure using support vector machine classifier from sklearn import svm clf svm svckernel 'rbf 'clf fitx_train y_train y_prediction clf predict x_test from sklearn metrics import confusion_matrix print confusion_matrix y_test y_prediction )[[ ] system callsurl... |
15,258 | / return start timeit time clock (print (euler ' constant is approximately {: }format myfun ( ,nnp log( ))end timeit time clock (print (elapsed time{: fseconds format (end start )euler ' constant is approximately elapsed time seconds now remove the character before the character in the code abovein order to activate th... |
15,259 | [ ahalta krishnamurthyp chenand melton competitive learning algorithms for vector quantization neural networks : - [ akaike new look at the statistical model identification ieee transactions on automatic control ( ): - [ aronszajn theory of reproducing kernels transactions of the american mathematical society : - [ art... |
15,260 | bibliography [ billingsley probability and measure john wiley sonsnew yorkthird edition [ bishop pattern recognition and machine learning springernew york [ boggs and byrd adaptivelimited-memory bfgs algorithms for unconstrained optimization siam journal on optimization ( ): - [ botevj grotowskiand kroese kernel densit... |
15,261 | [ cinlar introduction to stochastic processes prentice hallenglewood cliffs [ cover and thomas elements of information theory john wiley sonsnew york [ danielw graggl kaufmanand stewart reorthogonalization and stable algorithms for updating the gram-schmidt qr factorization mathematics of computation ( ): - [ - de boer... |
15,262 | [ freund and schapire decision-theoretic generalization of on-line learning and an application to boosting comput syst sci ( ): - [ friedman greedy function approximationa gradient boosting machine annals of statistics : - [ gelman bayesian data analysis chapman hallnew yorksecond edition [ gelman and hall data analysi... |
15,263 | [ kallenberg foundations of modern probability springernew yorksecond edition [ karalic linear regression in regression tree leaves in proceedings of ecai- pages - hoboken john wiley sons [ kaynak methods of combining multiple classifiers and their applications to handwritten digit recognition master' thesisinstitute o... |
15,264 | [ 'ecuyer good parameters and implementations for combined multiple recursive random number generators operations research ( ): [ lehmann and casella theory of point estimation springernew yorksecond edition [ lewis and payne generalized feedback shift register pseudorandom number algorithm journal of the acm ( ): - [ ... |
15,265 | [ nielsen neural networks and deep learningvolume determination press [ petersen and pedersen the matrix cookbook technical university of denmark [ quinlan learning with continuous classes in adams and sterlingeditorsproceedings ai' pages - singapore world scientific [ rasmussen and williams gaussian processes for mach... |
15,266 | [ shor minimization methods for non-differentiable functions springerberlin [ silverman density estimation for statistics and data analysis chapman hallnew york [ simonoff smoothing methods in statistics springernew york [ steinwart and christmann support vector machines springernew york [ strang introduction to linear... |
15,267 | auxiliary variable methods axioms of kolmogorov acceptance probability - acceptance-rejection method accuracy (classification-) activation function adaboost - adagrad adam method adjoint operation affine transformation agglomerative clustering akaike information criterion algebraic multiplicity aligned arrays (python) ... |
15,268 | beta distribution bias of an estimator bias vector (deep learning) bias-variance tradeoff binomial distribution boltzmann distribution bootstrap aggregationsee bagging bootstrap method bounded mapping boxplot broadcasting (python) broyden' family broyden-fletcher-goldfarb-shanno (bfgsupdating burn-in period categorical... |
15,269 | credible interval region critical region value cross tabulate cross-entropy method risk in-sample training loss cross-validation leave-one-out linear model crude monte carlo cubic spline cumulative distribution function (cdf) joint cycle davidon-fletcher-powell updating decision tree deep learning degrees of freedom de... |
15,270 | bayes cdf distribution entropy impurity epoch (deep learning) equilikely principle ergodic markov chain error of the first and second kind estimate estimator bias of control variable efficiency of unbiased euclidean norm evaluation functional event elementary independent exact match ratio exchangeable variables expecta... |
15,271 | boosting descent gram matrix gram-schmidt procedure hamming distance hermite polynomials hermitian matrix hessian matrix hidden layer hierarchical classification hilbert matrix inverse hilbert space isomorphism hinge loss histogram hoeffding' inequality homotopy paths hyperparameters hypothesis testing immutable (pytho... |
15,272 | laplace' approximation lasso (regression) latent variable methodssee auxiliary variable methods law of large numbers law of total probability learner learning rate least-squares iterative reweighted nonlinear ordinary regularized leave-one-out cross-validation left pseudo-inverse left-eigenvector legendre polynomials l... |
15,273 | implementing svm in python svm kernels pros and cons of svm classifiers classification algorithms decision tree introduction to decision tree implementing decision tree algorithm building tree implementation in python classification algorithms naive bayes introduction to naive bayes algorithm building model using naive... |
15,274 | types of linear regression multiple linear regression (mlr python implementation assumptions machine learning algorithms clustering clustering algorithms overview introduction to clustering cluster formation methods measuring clustering performance silhouette analysis analysis of silhouette score types of ml clustering... |
15,275 | role of dendrograms in agglomerative hierarchical clustering machine learning algorithms knn algorithm knn algorithm finding nearest neighbors introduction working of knn algorithm implementation in python knn as classifier knn as regressor pros and cons of knn applications of knn machine learning algorithms performanc... |
15,276 | machine learning with python basics we are living in the 'age of datathat is enriched with better computational power and more storage resourcesthis data or information is increasing day by daybut the real challenge is to make sense of all the data businesses organizations are trying to deal with it by building intelli... |
15,277 | dynamic scenarios there are some scenarios which are dynamic in nature they keep changing over time in case of these scenarios and behaviorswe want machine to learn and take data-driven decisions some of the examples can be network connectivity and availability of infrastructure in an organization difficulty in transla... |
15,278 | based on the abovethe following diagram represents machine learning modeltask (tperforman ce (pexperienc (elet us discuss them more in detail nowtask(tfrom the perspective of problemwe may define the task as the real-world problem to be solved the problem can be anything like finding best house price in specific locati... |
15,279 | performance (pan ml algorithm is supposed to perform task and gain experience with the passage of time the measure which tells whether ml algorithm is performing as per expectation or not is its performance (pp is basically quantitative metric that tells how model is performing the tasktusing its experiencee there are ... |
15,280 | customer segmentation object recognition fraud detection fraud prevention recommendation of products to customer in online shopping |
15,281 | machine learning with pythonmachine python ecosystem an introduction to python python is popular object-oriented programing language having the capabilities of highlevel programming language its easy to learn syntax and portability capability makes it popular these days the followings facts gives us the introduction to... |
15,282 | huge number of modulespython has huge number of modules for covering every aspect of programming these modules are easily available for use hence making python an extensible language support of open source communityas being open source programming languagepython is supported by very large developer community due to thi... |
15,283 | on windows platform with the help of following stepswe can install python on windows platformfirstgo to nextclick on the link for windows installer python-xyz msi file here xyz is the version we wish to install nowwe must run the file that is downloaded it will take us to the python install wizardwhich is easy to use n... |
15,284 | you can also check this in detailed video lecture at anaconda asp why python for data sciencepython is the fifth most important language as well as most popular language for machine learning and data science the following are the features of python that makes it the preferred choice of language for data scienceextensiv... |
15,285 | components of python ml ecosystem in this sectionlet us discuss some core data science libraries that form the components of python machine learning ecosystem these useful components make python an important language for data science though there are many such componentslet us discuss some of the importance components ... |
15,286 | after pressing enterit will start notebook server at localhost: of your computer it is shown in the following screen shotnowafter clicking the new tabyou will get list of options select python and it will take you to the new notebook for start working in it you will get glimpse of it in the following screenshots |
15,287 | on the other handif you are using standard python distribution then jupyter notebook can be installed using popular python package installerpip pip install jupyter types of cells in jupyter notebook the following are the three types of cells in jupyter notebookcode cellsas the name suggestswe can use these cells to wri... |
15,288 | operations associated with linear algebra we can also see numpy as the replacement of matlab because numpy is mostly used along with scipy (scientific pythonand mat-plotlib (plotting libraryinstallation and execution if you are using anaconda distributionthen no need to install numpy separately as it is already install... |
15,289 | data frames for examplethe following table shows the data of students having their names and roll numbersage and gendername roll number age gender aarav male harshit male kanika female mayank male panelit is -dimensional data structure containing heterogeneous data it is very difficult to represent the panel in graphic... |
15,290 | example the following is an example of creating series from ndarray by using pandasin [ ]import pandas as pd in [ ]import numpy as np in [ ]data np array([' ',' ',' ',' ',' ',' ']in [ ] pd series(datain [ ]print ( dtypeobject for more detailed study of pandas you can go to the link scikit-learn another useful and most ... |
15,291 | from sklearn datasets import load_breast_cancer on the other handif you are using standard python distribution and having numpy and scipy then scikit-learn can be installed using popular python package installerpip pip install - scikit-learn after installing scikit-learnyou can use it into your python script as you hav... |
15,292 | python machine learning methods for machine learning there are various ml algorithmstechniques and methods that can be used to build models for solving real-life problems by using data in this we are going to discuss such different kinds of methods different types of methods the following are various ml methods based o... |
15,293 | classification the key objective of classification-based tasks is to predict categorial output labels or responses for the given input data the output will be based on what the model has learned in training phase as we know that the categorial output responses means unordered and discrete valueshence each output respon... |
15,294 | dimensionality reduction this unsupervised ml method is used to reduce the number of feature variables for each data sample by selecting set of principal or representative features question arises here is that why we need to reduce the dimensionalitythe reason behind is the problem of feature space complexity which ari... |
15,295 | step nowwe can update the strategies if it is required so step at lastrepeat steps - until the agent got to learn and adopt the optimal policies tasks suited for machine learning the following diagram shows what type of task is appropriate for various ml problemsis data correlated or redundantyes no dimensionality redu... |
15,296 | step nowstart the training of model by providing whole training data in one go step nextstop results/performance learning/training process once you got satisfactory step finallydeploy this trained model into production hereit will predict the output for new data sample online learning it is completely opposite to the b... |
15,297 | machine learning with python -machine datalearning loading for ml projects suppose if you want to start ml project then what is the first and most important thing you would requireit is the data that we need to load for starting any of the ml project with respect to datathe most common format of data for ml projects is... |
15,298 | quotes in csv data filesdouble quotation ("mark is the default quote character it is important to consider the role of quotes while uploading the csv file into ml projects because we can also use other quote character than double quotation mark but in case of using different quote character than standard onewe must hav... |
15,299 | we can print the names of the headers with the following line of scriptprint(headersthe following line of script will print the shape of the data number of rows columns in the fileprint(data shapenext script line will give the first three line of data fileprint(data[: ]output ['sepal_length''sepal_width''petal_length''... |
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