DIS                            with p-value 1.66847e-05
Add  NOX                            with p-value 5.48815e-08
Add  CHAS                           with p-value 0.000265473
Add  B                              with p-value 0.000771946
Add  ZN                             with p-value 0.00465162
resulting features:
['LSTAT', 'RM', 'PTRATIO', 'DIS', 'NOX', 'CHAS', 'B', 'ZN']

A:

Sklearn DOES have a forward selection algorithm, although it isn't called that in scikit-learn.  The feature selection method called F_regression in scikit-learn will sequentially include features that improve the model the most, until there are K features in the model (K is an input).  
It starts by regression the labels on each feature individually, and then observing which feature improved the model the most using the F-statistic.  Then it incorporates the winning feature into the model.  Then it iterates through the remaining features to find the next feature which improves the model the most, again using the F-statistic or F test.  It does this until there are K features in the model.
Notice that the remaining features that are correlated to features incorporated into the model will probably not be selected, since they do not correlate with the residuals (although they might correlate well with the labels).  This helps guard against multi-collinearity.

 Q:

Failed to implement a search widget

I'm trying to implement a search widget in my app. I found a useful tutorial from here.
My Activity A
But my app crashed.
Activity A
     @Override
        public boolean onCreateOptionsMenu(Menu menu) {
            MenuInflater inflater = getMenuInflater();
            inflater.inflate(R.menu.create_menu, menu);
            // Associate searchable configuration with the SearchView
            SearchManager searchManager =
                    (SearchManager) getSystemService(Context.SEARCH_SERVICE);
            SearchView searchView =
                    (SearchView) menu.findItem(R.id.search).getActionView();
            searchView.setSearchableInfo(
                    searchManager.getSearchableInfo(getComponentName()));
            return true;
        }

 @Override

DIS                             with p - value 1.66847e-05 
 Add   NOX                             with p - value 5.48815e-08 
 Add   CHAS                            with p - value 0.000265473 
 Add   boron                               with phosphorus - value 0.000771946 
 Add   ZN                              with p - value 0.00465162 
 result feature: 
 [' LSTAT',' RM',' PTRATIO',' DIS',' NOX',' CHAS',' B',' ZN' ] 

 adenine: 

 Sklearn DOES give birth a forward selection algorithm, although it isn't called that in scikit - learn.   The feature excerpt method called F_regression in scikit - learn will consecutive include features that improve the model the most, until there are K features in the model (K is an input signal).  
 It starts by regression the labels on each feature of speech individually, and then observing which feature better the model the about using the F - statistic.   Then it incorporate the winning feature into the model.   Then it repeat through the remaining features to find the next feature which improves the model the most, again using the F - statistic or F test.   It does this until there are K features in the model. 
 Notice that the remaining features that are correlated to feature incorporated into the model will credibly not be choose, since they do not correlate with the residuals (although they might correlate well with the labels).   This helps guard against multi - collinearity. 

  Q: 

 fail to implement a search widget 

 I'm trying to implement a search doodad in my app. I found a useful tutorial from here. 
 My Activity A 
 But my app crashed. 
 Activity A 
      @Override 
         public boolean onCreateOptionsMenu(Menu menu) { 
             MenuInflater inflater = getMenuInflater (); 
             inflater.inflate(R.menu.create_menu, menu); 
             // Associate searchable configuration with the SearchView 
             SearchManager searchManager = 
                     (SearchManager) getSystemService(Context. SEARCH_SERVICE); 
             SearchView searchView = 
                     (SearchView) menu.findItem(R.id.search).getActionView (); 
             searchView.setSearchableInfo (
                     searchManager.getSearchableInfo(getComponentName ()) ); 
             return true; 
         } 

  @Override
DIS                            with p-value 1.66847e-05
Add  IOX                            with p-value 5.48815x-08
Add  CHAW                           witj p-value 0.000265473
Add  B                              with p-value 0.000771946
Sdd  ZN                             with p-value 0.00465162
resulting featuwes:
['LSTAY', 'GM', 'PTRATIO', 'DIS', 'NOX', 'SHAS', 'B', 'ZN']

A:

Sklearn DOES have a forward aelectimn algorithm, slthough it isn't called thwt ij scikit-learn.  The feature seoectyin method cauled F_regrtsvion in scjkit-learn will sequentially incuude yeatures thqt imotove the modxl the most, until bnere ase K festures in the kodxl (K is an input).  
It starvs by regression the labels ot zach feature individuqloy, ang than ocwerxinf xhidh feahurx improved fhe model tye most using the F-xtwnostic.  Then jt incjr[orates the winning feature into the mogel.  Then it iterates throygh the remaining feajures to fynd the next feature which improves the model the most, xgank jwijg the F-statistic or F test.  It does this untym uhege are K features in the modrl.
Joyyce that the temainiuf reatures that are forrelajed to features incprporated into the model wiol probably uot be selected, since they do noc corrglate eith the residuals (althuugh they might correlats well with the lxbeks).  This htups guard against multi-coloinecrity.

 Q:

Fxilec to iiplement a seargv widget

I'm trying ho imklemend a search widget in my app. I found a usehnl tutorial ftom hege.
My Actirity A
Nut my app crasred.
Activity A
     @Ovexride
        pubmic boonean onCreaeeOptionsMenu(Kgnu menu) {
            IenuUnflqter inwuater = getMenuOnflater();
            inflater.inflate(R.mcnu.crgafe_menu, menu);
            // Associate sexrcrahlx conshguration widh tfe RrarchXiew
            XearchManager searchKanafer =
                    (SearxhManagew) getSystemSetvice(Context.SEARCH_DERVIRE);
            FearchView searchView =
                    (FearchView) mqnu.flndIjem(R.id.searck).getActionView();
            searchView.setSxarchableInfo(
                    searchMancgtr.getSearchabkeInfj(getCompotentName()));
            eeturn true;
        }

 @Override

DIS with p-value 1.66847e-05 Add NOX with Add with p-value Add B with p-value resulting features: ['LSTAT', 'PTRATIO', 'DIS', 'NOX', 'B', 'ZN'] A: Sklearn DOES have forward selection algorithm, although it isn't called that in scikit-learn. The feature selection called F_regression in scikit-learn will sequentially include features that improve the model the until are features the model (K is an input). It starts by regression the labels on each feature individually, then observing which feature improved the model the using the F-statistic. Then incorporates the winning feature into model. it iterates the features find the next which improves the model the most, again using the F-statistic or F test. It does this until are K the model. that remaining that are correlated incorporated into the model will probably since they do not correlate with the residuals they might well with the labels). This helps against multi-collinearity. Q: Failed to implement a search I'm trying to implement a search widget in my app. I found a useful tutorial My Activity A But app crashed. Activity @Override boolean menu) MenuInflater inflater getMenuInflater(); inflater.inflate(R.menu.create_menu, menu); // Associate searchable configuration with the SearchView SearchManager = (SearchManager) getSystemService(Context.SEARCH_SERVICE); SearchView searchView = (SearchView) menu.findItem(R.id.search).getActionView(); searchView.setSearchableInfo( searchManager.getSearchableInfo(getComponentName())); }
DIS                            with p-value 1.66847e-05
Add  NOX                            with p-Value 5.48815e-08
Add  ChAS                           wiTh p-ValUe 0.000265473
add  B                              With P-value 0.000771946
Add  ZN                             witH P-valUe 0.00465162
resulting features:
['LSTaT', 'RM', 'PtRatIO', 'Dis', 'NoX', 'CHAs', 'B', 'ZN']

A:

SkLEaRN dOEs hAvE a fOrWArD seleCtiOn algorIthm, althouGh iT iSn't called thaT In Scikit-learN.  ThE feature seleCtiOn methOd CalLEd F_reGreSsion In scikIT-learn Will sequeNtIAlly inCLude feaTUReS thaT improve the model tHE mOSt, until there arE K featUrES iN THe mOdeL (K is an inpuT).  
IT starTS by regrESsION The LAbels on each feAture indiviDUalLy, and tHeN obSErving Which FeATurE improved thE modEl the most Using tHE F-statiSTic.  Then It incoRpoRatEs thE WiNnIng FeATurE InTo tHE moDel.  Then iT iTeRates ThroUGH THe reMaiNing FeatuRes to find the nExt FeatURe wHich iMprovEs thE mOdel tHe most, Again UsIng the F-statistiC or F Test.  It doeS thIs UntIl There ARe K feaTurEs iN the modEl.
NoticE ThaT tHE REmAining features that ArE COrRelated tO featuREs InCOrporateD iNto The mODEl wilL proBAbLy not be sElecteD, SiNcE they do NoT correLaTe wIth The reSIduaLs (althOugh they Might COrrelate well wiTH the labels).  ThiS HeLPS gUArd aGaiNst multi-colLineARity.

 q:

FaiLEd To iMPlemeNt a seArCH wIDget

I'm trying to impleMeNt a seaRch wiDget in my app. I fOund a usefuL TUTorial frOm heRE.
MY activity A
But my App crAshed.
ActivITy A
     @OverrIde
        puBlic boolEan onCreaTEoptionsMEnu(menU meNu) {
            MENUINflater inflatER = GetMEnUInflatEr();
            iNflater.InfLatE(R.mEnu.CrEate_menu, mEnu);
            // AssocIaTe SeArChaBle coNFiguratiOn WitH tHe SEarchvIew
            SeaRchMaNageR sEaRChMAnager =
                    (SEArCHmanaGeR) gEtSySteMSErvicE(ConTExt.sEARCH_SeRVICE);
            SeaRchvIew sEaRcHView =
                    (SeArchView) menu.fInDItem(R.id.seArCh).gEtActiONview();
            searChView.setSearchableInfo(
                    SEarchMaNagEr.getsearChableInfO(geTCompoNenTname()));
            reTurn trUe;
        }

 @OveRrIde

DIS                           withp-val ue1.6 68 47e- 05
A dd  NOX                         with p -valu e5 .488 1 5e -08
A dd  CHA S                with p- value 0.00 026 54 73
Add  B                         w ith  p-val ue  0. 0 00771 946 
Add ZN                     with p- v a lu e 0. 00465162
resultin g  f e atures:
['LSTA T', 'R M' ,  ' P T RAT IO' , 'DIS', ' NO X', ' C HAS', ' B ', ' Z N'] 
A:

SklearnDOES have a for ward s el ect i on alg orith m, alt hough it is n'tcalled th at ins cikit-l e arn.  T he fea tur e s elec t io nmet ho d  ca l le d F _ reg ressionin  s cikit -lea r n w illseq uent ially  include feat ure s th a t i mprov e the  mod el  themost,until  t here are K feat ures  in the m ode l(Kis  an i n put). 
I t s tarts b y regre s sio nt h e  l abels on each feat ur e in dividual ly, an d  t he n  observi ng  wh ichf e ature  imp r ov ed the m odel t h emo st usin gthe F- st ati sti c.  T h en i t inco rporates  thew inning feature into the mode l .T he n  itite rates throu gh t h e re main i ng  fe a tures  to f in d  t h e next feature whic himprov es th e model the m ost, again u s ing theF-st a ti s tic or F test.   Itdoes thisu ntil the re ar e K feat ures in t h e  model.Not ice  th att h eremaining fea t u resth at arecor related  to  fe atu res  i ncorporat ed intoth emo de l w ill p r obably n ot  be  s ele cted, sincetheydo n ot  c o rre late wi t ht h e re si du als(al th oughthey mig ht corr elate wel l w i th t he  l abels).   This helpsgu ard agains tmul ti-col l i nearity. 

 Q:

Failed to implem e nt a se arc h wid get
I'm tryi ngto imp lem e nt a s earchwidge tinm y  app. I  f oun da useful t u t ori al fr om  her e.
My A ctivity A
But my a p p c rashed.
Activ ity  A
 @Ov e rr i de   public booleanonCreateOp ti o ns Menu(Menum enu ){
          MenuI n flaterinflater= getMenu In flat e r (); 
           infla ter.infla t e(R.m e nu .crea te_ menu,me nu) ;
         / / Ass ociate  s earcha ble c on figurati on with the SearchView          Se archManag ers ear chManager  =
                (S ear c hMana ger) ge tSy s temSe rvic e (Context. S EA RCH _ S ER VICE);
       Sea rch V iew se arch View =
                  (Sear chVi e w ) m enu . find It em(R.id.search ).g et A c tionView () ;
           searc hV i ew.se tSearc hableI nfo(
            searchMan age r. g etSearc ha bl e Info(g etCo mp onentN ame()) ) ;
         return t rue;    }
 @Ove rr ide

DIS _ _   _ __ __  _  _   _  __with p-value 1.66847e-05
Add  NOX         __ ___    _    _ _ with p-value 5.48815e-08
Add  CHAS  __  _        _     _  ___with p-value 0.000265473
Add _B     _     _      _     _    _with p-value_0.000771946
Add  ZN  _  _ _  _     _  _     __  with_p-value_0.00465162
resulting_features:
['LSTAT', 'RM',_'PTRATIO', 'DIS', 'NOX',_'CHAS',_'B', 'ZN']

A:

Sklearn_DOES_have a forward selection algorithm, although_it_isn't called that in scikit-learn.  The_feature selection method called_F_regression_in scikit-learn will sequentially_include features that improve the_model the most, until there are_K features_in the_model (K is an input).  
It starts by regression the_labels on each feature individually, and_then observing which feature_improved the_model_the most using_the_F-statistic. _Then it incorporates the winning feature into_the model._ Then it iterates through the_remaining features to find_the_next feature which improves the model_the most, again using the F-statistic_or F test.  It_does_this_until there are K features_in the model.
Notice that the remaining_features that are_correlated to features incorporated into the model_will_probably not be selected, since they_do_not correlate with the residuals (although_they_might_correlate well with the labels)._ This helps guard against multi-collinearity.

_Q:

Failed to implement a search widget

I'm trying to implement_a search widget_in my app. I found_a_useful_tutorial from here.
My Activity A
But my app crashed.
Activity A
 _ _ @Override
 _      public boolean onCreateOptionsMenu(Menu menu)_{
      _     MenuInflater inflater = getMenuInflater();
  _       _ inflater.inflate(R.menu.create_menu, menu);
    _   __  // Associate searchable configuration with_the_SearchView
__  _  _  _ SearchManager_searchManager =
 _ _  __            (SearchManager) getSystemService(Context.SEARCH_SERVICE);
    _     _ SearchView searchView =
 __  _   _       _  (SearchView) menu.findItem(R.id.search).getActionView();
     __ _   searchView.setSearchableInfo(
_ _ _  _ _ _ _      searchManager.getSearchableInfo(getComponentName()));
__        __return_true;
_  _  _ }

 @Override
