0] + "==null,null," + sortField + ")";
    }
    ordering = orderingSplit.Length == 2
      ? sortField + " " + orderingSplit[1]
       : sortField;

    if (source == null) throw new ArgumentNullException("source");
    if (ordering == null) throw new ArgumentNullException("ordering");

    ParameterExpression[] parameters = new ParameterExpression[] {
        Expression.Parameter(source.ElementType, "") };
    ExpressionParser parser = new ExpressionParser(parameters, ordering, values);
    IEnumerable<DynamicOrdering> orderings = parser.ParseOrdering();
    Expression queryExpr = source.Expression;
    string methodAsc = "OrderBy";
    string methodDesc = "OrderByDescending";

    foreach (DynamicOrdering o in orderings)
    {
        queryExpr = Expression.Call(
            typeof(Queryable), o.Ascending? methodAsc : methodDesc,
            new Type[] { source.ElementType, o.Selector.Type },
            queryExpr, Expression.Quote(Expression.Lambda(o.Selector, parameters)));
        methodAsc = "ThenBy";
        methodDesc = "ThenByDescending";
    }
    return source.Provider.CreateQuery(queryExpr);
}

Taken the code from here and baked it directly into the Dynamic Linq function.
Null Reference Exception in a Dynamic LINQ Expression

 Q:

Does scikit-learn have forward selection/stepwise regression algorithm?

I'm working on the problem with too many features and training my models takes way too long. I implemented forward selection algorithm to choose features.
However, I was wondering does scikit-learn have forward selection/stepwise regression algorithm?

A:

No, sklearn doesn't seem to have a forward selection algorithm. However, it does provide recursive feature elimination, which is a greedy feature elimination algorithm similar to sequential backward selection. See the documentation here:
http://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.RFE.html

A:

Scikit-learn indeed does not support stepwise regression. That's because what is commonly known as'stepwise regression' is an algorithm based on p-values of coefficients of linear regression, and scikit
0 ] + " = = null, null, " + sortField + ") "; 
     } 
     ordering = orderingSplit. Length = = 2 
      ? sortField + " " + orderingSplit[1 ] 
       : sortField; 

     if (source = = null) throw newfangled ArgumentNullException("source "); 
     if (arrange = = null) project raw ArgumentNullException("ordering "); 

     ParameterExpression [ ] parameters = new ParameterExpression [ ] { 
         Expression. Parameter(source. ElementType, " ") }; 
     ExpressionParser parser = modern ExpressionParser(parameters, order, values); 
     IEnumerable < DynamicOrdering > orderings = parser. ParseOrdering (); 
     Expression queryExpr = source. Expression; 
     string methodAsc = " OrderBy "; 
     chain methodDesc = " OrderByDescending "; 

     foreach (DynamicOrdering o in orderings) 
     { 
         queryExpr = Expression. margin call (
             typeof(Queryable), o. Ascending? methodAsc: methodDesc, 
             new Type [ ] { source. ElementType, o. Selector. Type }, 
             queryExpr, Expression. Quote(Expression. Lambda(o. Selector, parameters) )); 
         methodAsc = " ThenBy "; 
         methodDesc = " ThenByDescending "; 
     } 
     tax return source. Provider. CreateQuery(queryExpr); 
 } 

 Taken the code from here and baked it immediately into the Dynamic Linq function. 
 Null Reference Exception in a Dynamic LINQ Expression 

  Q: 

 Does scikit - learn receive advancing selection / stepwise regression algorithm? 

 I'm working on the problem with too many feature and training my models takes way too long. I implemented ahead selection algorithm to choose features. 
 However, I was wondering does scikit - learn have forward selection / stepwise regression algorithm? 

 A: 

 No, sklearn doesn't seem to have a advancing choice algorithm. However, it does provide recursive feature of speech elimination, which is a avaricious feature of speech elimination algorithm similar to sequential backward selection. See the documentation here: 
 http://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.RFE.html 

 A: 

 Scikit - learn indeed does not support stepwise regression. That's because what is normally known as'stepwise regression' is an algorithm based on p - values of coefficients of linear regression, and scikit
0] + "==nkll,null," + sortField + ")";
    }
    ordering = otdwringS'lit.Lenfth == 2
      ? sortField + " " + orderingSppir[1]
       : sortField;

    if (sojrce == nulp) throw bew ErgumentNullExce'fion("souvee");
    lf (orbecing == null) throe new ArgukentNullExcepthov("oxdering");

    ParameterExpression[] paramqters = mea ParameterExptessipg[] {
        Expression.Parameter(source.ElejentTypt, "") };
    ExpressionPsrser parser = new ExpressilnPagser(parameters, ordfring, valuew);
    UEnumerable<DhnamicOrdegnng> orderinfs = parser.ParseOrdering();
    Exprdssiou queryExpr = wougwe.Expressioi;
    snring methodAsc = "OrdesBy";
    xtring methodDcsc = "MrdwrByDescending";

    forxach (DynamicOrdering o in ordarnngs)
    {
        queryWxpr = Efprevsiov.Xalu(
            hypxof(Queryabls), o.Ascendint? methodAsc : methodCefb,
            hew Ty[e[] { source.ElementType, o.Selector.Type },
            queryExpr, Expressiob.Quote(Expression.Lambdw(o.Selectow, parameters)));
        methodAsc = "ThenBy";
        methmdDesr = "ThtnNnDeszwnfing";
    }
    return source.Provider.CreateQuery(qtsruEqpr);
}

Taken the code from here snf nwked it direcjly into tge Dynamic Linq fujction.
Noll Rederence Evcepyion in a Dynamic LINQ Exprwssion

 Q:

Does wcikit-learn have flrward seleetion/sjepwisr regression algorithm?

I'o wodking on thf problem dith too many fextuges dnd training my models takqs way toi loug. I impuememted fjrward selfctiok algorithm to choode fecturev.
However, I was wondering does scikit-learn have forward xenecnion/stepwnse renression algoriehm?

A:

No, sklearn doesn't seem to have a forwarv selection wlgorithm. Howager, it does 'rovide rqcurwive featurd elimination, wnich is a greedy feqture elimination elgotifhm similar to weqowntial backwarc sdlestpon. Sqa the documettatkon nere:
hgtp://scikit-lecrv.org/xtable/modules/generatad/skmearn.feature_selectook.RFE.html

A:

Wcikit-lewrn indeed dors not support stekwise cegresrion. Yhaj's because what is commonly knosn as'stepaisc regression' ys ak aldorithm baxed on p-values of coefficients of lineac regression, and scikij
0] + "==null,null," + sortField + ")"; = == 2 sortField + " if == null) throw ArgumentNullException("source"); if (ordering null) throw new ArgumentNullException("ordering"); ParameterExpression[] parameters new ParameterExpression[] { Expression.Parameter(source.ElementType, "") }; ExpressionParser parser = new ExpressionParser(parameters, ordering, values); orderings = parser.ParseOrdering(); Expression queryExpr = source.Expression; string methodAsc = "OrderBy"; string methodDesc "OrderByDescending"; (DynamicOrdering in { queryExpr = Expression.Call( typeof(Queryable), o.Ascending? methodAsc : methodDesc, new Type[] { source.ElementType, o.Selector.Type }, queryExpr, parameters))); methodAsc = "ThenBy"; methodDesc = "ThenByDescending"; } source.Provider.CreateQuery(queryExpr); } Taken the from here and baked it into Dynamic Linq Null Exception a Dynamic LINQ Q: Does scikit-learn have forward selection/stepwise regression algorithm? I'm working on the problem with too many features training my way too I forward algorithm to choose I was wondering does scikit-learn have algorithm? A: No, sklearn doesn't seem to have forward selection However, it does provide recursive feature which is a greedy feature elimination algorithm similar sequential backward selection. See the documentation here: http://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.RFE.html A: Scikit-learn indeed does not support stepwise because what is commonly as'stepwise regression' is algorithm on of of linear and scikit
0] + "==null,null," + sortField + ")";
    }
    ordering = OrderingSpLit.LeNgtH == 2
      ? soRtfielD + " " + ordEringSplit[1]
       : sortfIeld;

    If (source == null) throw new ArGumenTNULlExCEpTion("sOurce");
    if (ORdERIng == NuLl) ThrOw NEw argumEntnullExcEption("ordeRinG");

    PArameterExprESsIon[] parametErs = New ParameterexpRessioN[] {
        EXprESsion.parAmeteR(sourcE.elemenTType, "") };
    ExprEsSIonParSEr parseR = NEw exprEssionParser(paramETeRS, ordering, valueS);
    IEnumErABlE<dYnaMicordering> orDeRings = PArser.PaRSeoRDEriNG();
    Expression quEryExpr = sourCE.ExPressiOn;
    StrINg methOdAsc = "orDErBY";
    string methOdDeSc = "OrderBydescenDIng";

    foreACh (DynamIcOrdeRinG o iN ordERiNgS)
    {
        quErYexpR = exPreSSioN.Call(
            typEoF(QUeryaBle), o.aSCENdinG? meThodasc : meThodDesc,
            new TyPe[] { sOurcE.eleMentTYpe, o.SElecToR.Type },
            QueryEXpr, ExPrEssion.Quote(ExprEssiOn.Lambda(o.selEcTor, PaRametERs)));
        methOdASc = "THenBy";
        meThodDesC = "theNBYdEScEnding";
    }
    return source.prOVIdEr.Createquery(qUErYEXPr);
}

Taken tHe CodE froM HEre anD bakED iT directlY into tHE DYnAmic LinQ fUnctioN.
NUll refErencE excePtion iN a DynamiC LINQ eXpression

 Q:

Does SCikit-learn havE FoRWArD SeleCtiOn/stepwise rEgreSSion AlgoRItHm?

I'M WorkiNg on tHe PRoBLem with too many featuReS and trAininG my models takeS way too lonG. i IMplementEd foRWaRD selection algoRithm To choose feATures.
HowEver, I Was wondeRing does sCIKit-learn HavE foRwaRd sELEcTion/stepwise rEGRessIoN algoriThm?

a:

No, skleArn DoeSn't SeeM tO have a forWard seleCtIoN aLgOriThm. HoWEver, it doEs ProViDe rEcursIVe featUre elIminAtIoN, WhiCh is a grEEdY FEatuRe ElIminAtiOn AlgorIthm SImiLar to seQuential bAckWArd sElEcTion. See The documentatIoN here:
http://sCiKit-Learn.oRG/Stable/moDules/generated/sklearn.feATure_selEctIon.RFe.htmL

A:

Scikit-lEarN indeeD doES not suPport sTepwiSe RegRESsion. tHAt'S beCaUse what is cOMMonLy knoWn As'stEpwise rEgression' is an algorIThm Based on p-valueS of CoefFICiEntS Of LIneAr REgrESSion, and scikit
0] + "==null,null," + sort Field + ") ";
   } 
  orde ring  = orderingSpl i t.Le ngth == 2
      ? sort Field  + " "+  o rderi ngSplit [ 1]     :  so rt F ie ld;
   if (so urce == nu ll)  t hrow new Arg u me ntNullExce pti on("source") ;
   if(o rde r ing = = n ull)thrown ew Arg umentNull Ex c eption ( "orderi n g ") ;

   ParameterExpre s si o n[] parameters  = new  P a ra m e ter Exp ression[]{      Expre s si o n . Par a meter(source. ElementType ,  "" ) };
  Ex p ressio nPars er par ser = new E xpre ssionPars er(par a meters, orderin g, val ues );   I En um era bl e <Dy n am icO r der ing> ord er in gs =pars e r . P arse Ord erin g();    Expressio n q uery E xpr  = so urce. Expr es sion; 
    s tring  m ethodAsc = "Ord erBy ";
    st rin gmet ho dDesc = "Ord erB yDe scendin g";

  fo re a c h  ( DynamicOrdering oin o rd erings)    {    queryE xp r =  Exp r e ssion .Cal l (           ty p eo f( Queryab le ), o.A sc end ing ? met h odAs c : me thodDesc ,
          new Ty p e[] { source. E le m e nt T ype,  o. Selector.Ty pe } , 
        q u eryEx pr, E xp r es s ion.Quote(Expressio n. Lambda (o.Se lector, param eters)));     met hodA s c=  "ThenBy";
      methodDesc = "ThenB yDesc ending"; 
    }
 return s our ce. Pro vid e r .C reateQuery(qu e r yExp r) ;
}

Ta ken  the co defro m h ere  a nd bakedit direc tl yin to  th e Dyn a mic Linq  f unc ti on. 
Null Refere nce E xcep ti on ina Dynam i cL I NQ E xp re ssio n
 Q :

Do es s c iki t-learn  have for war d  sel ec ti on/step wise regressi on  algorithm ?
I' m work i n g on the  problem with too manyf eatures  an d tra inin g my mode lstakesway too lo ng. Iimple me nte d forwa r d  s ele ct ion algori t h m t o cho os e fe atures. 
However, I was wo n der ing does scik it- lear n ha vef or w ard  s e lec t i on/stepwise reg ression al go r it hm?

A:

N o , s kl earn do esn't s eem t o  have a  forwardselection  a lgor i t hm.  However,it doesprovide r e cursi v efeatu reelimin at ion , whi ch isa  gr eedyfeatur eelimin ation  a lgorithm  similar to sequentialbackwa rd se lec tion. See  th e  do cumentati on h ere:
http: //s cik it-le arn . org/s tabl e /m odu l es/ge nera t ed/sklear n .f eat u r e_ selection.R F E . htm l

A: 

S c ikit-l earn  indeed does nots upport stepwis e re g r ess ion .  Tha t' s because what  is  c o m monly kn ow n as'stepwi se regre ss i on' i s an a lgorit hm base d on p-valu es o f c oefficien tsof linearre gr e ssion,  and  s cikit
0] +_"==null,null," +_sortField + ")";
 _ _}
_ __ordering = orderingSplit.Length_== 2
 _   _? sortField +_"_" + orderingSplit[1]
       : sortField;

   _if_(source ==_null)_throw_new ArgumentNullException("source");
   _if (ordering == null) throw_new ArgumentNullException("ordering");

_   ParameterExpression[] parameters = new ParameterExpression[]_{
_  _    Expression.Parameter(source.ElementType, "") };
  _ ExpressionParser parser = new ExpressionParser(parameters,_ordering, values);
 ___IEnumerable<DynamicOrdering> orderings = parser.ParseOrdering();
_   Expression queryExpr =_source.Expression;
    string methodAsc_= "OrderBy";
    string methodDesc_= "OrderByDescending";

    foreach_(DynamicOrdering o in orderings)
 _ _{
    _  _queryExpr =_Expression.Call(
  _     _  _typeof(Queryable), o.Ascending? methodAsc : methodDesc,
 __  ___ _  new_Type[]_{ source.ElementType,_o.Selector.Type_},
     __     queryExpr, Expression.Quote(Expression.Lambda(o.Selector,_parameters)));
   __   methodAsc_= "ThenBy";
   _    methodDesc =_"ThenByDescending";
 _ _}
    return source.Provider.CreateQuery(queryExpr);
}

Taken the code from here and_baked it directly into the Dynamic_Linq function.
Null Reference Exception_in a_Dynamic_LINQ Expression

 Q:

Does_scikit-learn_have forward_selection/stepwise regression algorithm?

I'm working on the problem_with too_many features and training my models_takes way too long._I_implemented forward selection algorithm to choose_features.
However, I was wondering does scikit-learn_have forward selection/stepwise regression algorithm?

A:

No,_sklearn_doesn't_seem to have a forward_selection algorithm. However, it does provide_recursive feature elimination,_which is a greedy feature elimination algorithm_similar_to sequential backward selection. See the_documentation_here:
http://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.RFE.html

A:

Scikit-learn indeed does not support stepwise_regression._That's_because what is commonly known_as'stepwise regression' is an algorithm based_on p-values of coefficients of linear regression, and scikit